Data Mining and the Tough Personal Information Privacy Sell Considered

Everyone come on in and have a seat, we will be starting this discussion a little behind schedule due to the fact we have a full-house here today. If anyone has a spare seat next to them, will you please raise your hands, we need to get some of these folks in back a seat. The reservations are sold out, but there should be a seat for everyone at today's discussion.

Okay everyone, I thank you and thanks for that great introduction, I just hope I can live up to all those verbal accolades.

Oh boy, not another controversial subject! Yes, well, surely you know me better than that by now, you've come to expect it. Okay so, today's topic is one about the data mining of; Internet Traffic, Online Searches, Smart Phone Data, and basically, storing all the personal data about your whole life. I know, you don't like this idea do you - or maybe you participate online in social online networks and most of your data is already there, and you've been loading up your blog with all sorts of information?

Now then, contemporary theory and real world observation of the virtual world predicts that for a fee, or for a trade in free services, products, discounts, or a chance to play in social online networks, employment opportunity leads, or the prospects of future business you and nearly everyone will give up some personal information.

So, once this data is collected, who will have access to it, who will use it, and how will they use it? All great questions, but first how can the collection of this data be sold to the users, and agreed upon in advance? Well, this can at times be very challenging; yes, very tough sell, well human psychology online suggests that if we give benefits people will trade away any given data of privacy.

Hold That Thought.

Let's digress a second, and have a reality check dialogue, and will come back to that point above soon enough, okay - okay agreed then.

The information online is important, and it is needed at various national security levels, this use of data is legitimate and worthy information can be gained in that regard. For instance, many Russian Spies were caught in the US using social online networks to recruit, make business contacts, and study the situation, makes perfect sense doesn't it? Okay so, that particular episode is either; an excuse to gather this data and analyze it, or it is a warning that we had better. Either way, it's a done deal, next topic.

And, there is the issue with foreign spies using the data to hurt American businesses, or American interests, or even to undermine the government, and we must understand that spies in the United States come from over 70 other nations. And let's not dismiss the home team challenge. What's that you ask? Well, we have a huge intelligence industrial complex and those who work in and around the spy business, often freelance on the side for Wall Street, corporations, or other interests. They have access to information, thus all that data mined data is at their disposal.

Is this a condemnation of sorts; No! I am merely stating facts and realities behind the curtain of created realities of course, without judgment, but this must be taken into consideration when we ask; who can we trust with all this information once it is collected, stored, and in a format which can be sorted? So, we need a way to protect this data for the appropriate sources and needs, without allowing it to be compromised - this must be our first order of business.

Let's Undigress and Go Back to the Original Topic at hand, shall we? Okay, deal.

Now then, what about large corporate collecting information; Proctor and Gamble, Ford, GM, Amazon, etc? They will certainly be buying this data from social networks, and in many cases you've already given up your rights to privacy merely by participating. Of course, all the data will help these companies refine their sorts using your preferences, thus, the products or services they pitch you will be highly targeted to your exact desires, needs, and demographics, which is a lot better than the current bombardment of Viagra Ads with disgusting titles, now in your inbox, deleted junk files.

Look, here is the deal...if we are going to collect data online, through social networks, and store all that the data, then we also need an excuse to collect the data first place, or the other option is not tell the public and collect it anyway, which we already probably realize that is now being done in some form or fashion. But let's for the sake of arguments say it isn't, then should we tell the public we are doing, or are going to do this. Yes, however if we do not tell the public they will eventually figure it out, and conspiracy theories will run rampant.

We already know this will occur because it has occurred in the past. Some say that when any data is collected from any individual, group, company, or agency, that all those involved should also be warned on all the collection of data, as it is being collected and by whom. Including the NSA, a government, or a Corporation which intends on using this data to either sell you more products, or for later use by their artificial intelligence data scanning tools.

Likewise, the user should be notified when cookies are being used in Internet searchers, and what benefits they will get, for instance; search features to help bring about more relevant information to you, which might be to your liking. Such as Amazon.com which tracks customer inquiries and brings back additional relevant results, most online shopping eCommerce sites do this, and there was a very nice expose on this in the Wall Street Journal recently.

Another digression if you will, and this one is to ask a pertinent question; If the government or a company collects the information, the user ought to know why, and who will be given access to this information in the future, so let's talk about that shall we? I thought you might like this side topic, good for you, it shows you also care about these things.

And as to that question, one theory is to use a system that allows certain trusted sources in government, or corporations which you do business with to see some data, then they won't be able to look without being seen, and therefore you will know which government agencies, and which corporations are looking at your data, and therefore there will be transparency, and there would have to be at that point justification for doing so. Or most likely folks would have a fit and then, a proverbial field day with the intrusion in the media.

Now then, one recent report from the government asks the dubious question; "How do we define the purpose for which the data will be used?"

Ah ha, another great question in this on-going saga indeed. It almost sounds as if they too were one of my concerned audience members, or even a colleague. Okay so, it is important not only to define the purpose of the data collection, but also to justify it, and it better be good. Hey, I see you are all smiling now. Good, because, it's going to get a bit more serious on some of my next points here.

Okay, and yes this brings about many challenges, and it is also important to note that there will be, ALWAYS more outlets for the data, which is collected, as time goes on. Therefore the consumer, investor, or citizen who allows their data to be compromised, stored for later use for important issues such as national security, or for corporations to help the consumer (in this case you) in their purchasing decisions, or for that company's planning for inventory, labor, or future marketing (most likely; again to whom; ha ha ha, yes you are catching on; You.

Thus, shouldn't you be involved at every step of the way; Ah, a resounding YES! I see from our audience today, and yes, I would have expected nothing less from you either. And as all this process takes place, eventually "YOU" are going to figure out that this data is out of control, and ends up everywhere. So, should you give away data easily?

No, and if it is that valuable, hold out for more. And then, you will be rewarded for the data, which is yours, that will be used on your behalf and potentially against you in some way in the future; even if it is only for additional marketing impressions on the websites you visit or as you walk down the hallway at the mall;

"Let's see a show of hands; who has seen Minority Report? Ah, most of you, indeed, if you haven't go see, it and you will understand what we are all saying up here, and others are saying in the various panel discussions this weekend."

Now you probably know this, but the very people who are working hard to protect your data are in fact the biggest purveyors of your information, that's right our government. And don't get me wrong, I am not anti-government, just want to keep it responsible, as much is humanly possible. Consider if you will all the data you give to the government and how much of that public record is available to everyone else;

    Tax forms to the IRS,
    Marriage licenses,
    Voting Registration,
    Selective Services Card,
    Property Taxes,
    Business Licenses,
    Etc.

The list is pretty long, and the more you do, the more information they have, and that means the more information is available; everywhere, about who; "YOU! That's who!" Good I am glad we are all clear on that one. Yes, indeed, all sorts of things, all this information is available at the county records office, through the IRS, or with various branches of OUR government. This is one reason we should all take notice to the future of privacy issues. Often out government, but it could be any first world government, claims it is protecting your privacy, but it has been the biggest purveyors of giving away our personal and private data throughout American history. Thus, there will a little bit of a problem with consumers, taxpayers, or citizens if they no longer trust the government for giving away such things as;

    Date of birth,
    Social Security number,
    Driver's license,
    Driving record,
    Taxable information,
    Etc., on and on.

And let's not kid ourselves here all this data is available on anyone, it's all on the web, much of it can be gotten free, some costs a little, never very much, and believe me there is a treasure trove of data on each one of us online. And that's before we look into all the other information being collected now.

Now then, here is one solution for the digital data realm, including smart phone communication data, perhaps we can control and monitor the packet flow of information, whereby all packets of info is tagged, and those looking at the data will also be tagged, with no exceptions. Therefore if someone in a government bureaucracy is looking at something they shouldn't be looking at, they will also be tagged as a person looking for the data.

Remember the big to do about someone going through Joe The Plumber's records in OH, or someone trying to release sealed documents on President Bush's DUI when he was in his 20s, or the fit of rage by Sara Palin when someone hacked her Yahoo Mail Account, or when someone at a Hawaii Hospital was rummaging through Barak Obama's certificate of showing up at the hospital as a baby, with mother in tow?

We need to know who is looking at the data, and their reason better be good, the person giving the data has a right-to-know. Just like the "right-to-know" laws at companies, if there are hazardous chemicals on the property. Let me speak on another point; Border Security. You see, we need to know both what is coming and going if we are to have secure borders.

You see, one thing they found with our border security is it is very important not only what comes over the border, which we do need to monitor, but it's also important to see what goes back over the border the other way. This is how authorities have been able to catch drug runners, because they're able to catch the underground economy and cash moving back to Mexico, and in holding those individuals, to find out whom they work for - just like border traffic - our information goes both ways, if we can monitor for both those ways, it keeps you happier, and our data safer.

Another question is; "How do we know the purpose for data being collected, and how can the consumer or citizen be sure that mass data releases will not occur, it's occurred in almost every agency, and usually the citizens are warned that their data was released or that the data base containing their information was breached, but that's after the fact, and it just proves that data is like water, and it's hard to contain. Information wants to be free, and it will always find a way to leak out, especially when it's in the midst of humans.

Okay, I see my time is running short here, let me go ahead and wrap it up and drive through a couple main points for you, then I'll open it up for questions, of which I don't doubt there will be many, that's good, and that means you've been paying attention here today.

It appears that we need to collect data for national security purposes research, planning, and for IT system for future upgrades. And collecting data for upgrades of an IT system, you really need to know about the bulk transfers of data and the time, which that data flows, and therefore it can be anonymized.

For national security issues, and for their research, that data will have anomalies in it, and there are problems with anomalies, because can project a false positives, and to get it right they have to continually refine it all. And although this may not sit well with most folks, nevertheless, we can find criminals this way, spies, terrorist cells, or those who work to undermine our system and stability of our nation.

With regards to government and the collection of data, we must understand that if there are bad humans in the world, and there are. And if many of those who shall seek power, may not be good people, and since information is power, you can see the problem, as that information and power will be used to help them promote their own agenda and rise in power, but it undermines the trust of the system of all the individuals in our society and civilization.

On the corporate front, they are going to try to collect as much data on you as they can, they've already started. After all, that's what the grocery stores are doing with their rewards program if you hadn't noticed. Not all the information they are collecting they will ever use, but they may sell it to third part affiliates, partners, or vendors, so that's at issue. Regulation will be needed in this regard, but the consumer should also have choices, but they ought to be wise about those choices and if they choose to give away personal information, they should know the risks, rewards, consequences, and challenges ahead.

Indeed, I thank you very much, and be sure to pick up a handout on your way out, if you didn't already get one, from the good looking blonde, Sherry, at the door. Thanks again, and let's take a 5-minute break, and then head into the question and answer session, deal?



Source: http://ezinearticles.com/?Data-Mining-and-the-Tough-Personal-Information-Privacy-Sell-Considered&id=4868392

Why Data Entry Outsourcing?

Data entry is the core of any business and though it may appear to be easy to manage and handle, this involves many processes that need to be dealt systematically. Huge changes have taken place in the field of data entry and due to this, handling work has become much easier then before. So if you want to make use of the best data entry services to maintain the data and other information about your company, then you need to have a professional company which provides data entry services with lowest possible rates and also within deadline.

Nowadays, it's becoming trend to outsource your Work to reliable service provider who provides excellent output out of their work. Many Companies or Organization prefer to outsource their data entry work to an offshore location. One of the key reasons why it has become so popular is the fact that the services they are providing from highly qualified professionals is cost effective and time bound.

Following are benefits of data entry outsourcing

o It helps you to focus on core business

o It reduces capital cost of infrastructure

o Competitive pricing which are as low as 40-60% of the prevailing US cost

o Remove management headaches

o Improves employee satisfaction with higher value addition jobs

o Use latest standard and new technology

o Quick turn around time and strong quality

o Make best use of competitive resources available worldwide

o High speed and low cost communication

o Line data processing possible from any location

Boost up your business by outsourcing data entry work.



Source: http://ezinearticles.com/?Why-Data-Entry-Outsourcing?&id=1350362

Outsource Data Mining Services to Offshore Data Entry Company

Companies in India offer complete solution services for all type of data mining services.

Data Mining Services and Web research services offered, help businesses get critical information for their analysis and marketing campaigns. As this process requires professionals with good knowledge in internet research or online research, customers can take advantage of outsourcing their Data Mining, Data extraction and Data Collection services to utilize resources at a very competitive price.

In the time of recession every company is very careful about cost. So companies are now trying to find ways to cut down cost and outsourcing is good option for reducing cost. It is essential for each size of business from small size to large size organization. Data entry is most famous work among all outsourcing work. To meet high quality and precise data entry demands most corporate firms prefer to outsource data entry services to offshore countries like India.

In India there are number of companies which offer high quality data entry work at cheapest rate. Outsourcing data mining work is the crucial requirement of all rapidly growing Companies who want to focus on their core areas and want to control their cost.

Why outsource your data entry requirements?

Easy and fast communication: Flexibility in communication method is provided where they will be ready to talk with you at your convenient time, as per demand of work dedicated resource or whole team will be assigned to drive the project.

Quality with high level of Accuracy: Experienced companies handling a variety of data-entry projects develop whole new type of quality process for maintaining best quality at work.

Turn Around Time: Capability to deliver fast turnaround time as per project requirements to meet up your project deadline, dedicated staff(s) can work 24/7 with high level of accuracy.

Affordable Rate: Services provided at affordable rates in the industry. For minimizing cost, customization of each and every aspect of the system is undertaken for efficiently handling work.

Outsourcing Service Providers are outsourcing companies providing business process outsourcing services specializing in data mining services and data entry services. Team of highly skilled and efficient people, with a singular focus on data processing, data mining and data entry outsourcing services catering to data entry projects of a varied nature and type.

Why outsource data mining services?

360 degree Data Processing Operations
Free Pilots Before You Hire
Years of Data Entry and Processing Experience
Domain Expertise in Multiple Industries
Best Outsourcing Prices in Industry
Highly Scalable Business Infrastructure
24X7 Round The Clock Services

The expertise management and teams have delivered millions of processed data and records to customers from USA, Canada, UK and other European Countries and Australia.

Outsourcing companies specialize in data entry operations and guarantee highest quality & on time delivery at the least expensive prices.


Source: http://ezinearticles.com/?Outsource-Data-Mining-Services-to-Offshore-Data-Entry-Company&id=4027029

Importance of Data Mining Services in Business

Data mining is used in re-establishment of hidden information of the data of the algorithms. It helps to extract the useful information starting from the data, which can be useful to make practical interpretations for the decision making.
It can be technically defined as automated extraction of hidden information of great databases for the predictive analysis. In other words, it is the retrieval of useful information from large masses of data, which is also presented in an analyzed form for specific decision-making. Although data mining is a relatively new term, the technology is not. It is thus also known as Knowledge discovery in databases since it grip searching for implied information in large databases.
It is primarily used today by companies with a strong customer focus - retail, financial, communication and marketing organizations. It is having lot of importance because of its huge applicability. It is being used increasingly in business applications for understanding and then predicting valuable data, like consumer buying actions and buying tendency, profiles of customers, industry analysis, etc. It is used in several applications like market research, consumer behavior, direct marketing, bioinformatics, genetics, text analysis, e-commerce, customer relationship management and financial services.

However, the use of some advanced technologies makes it a decision making tool as well. It is used in market research, industry research and for competitor analysis. It has applications in major industries like direct marketing, e-commerce, customer relationship management, scientific tests, genetics, financial services and utilities.

Data mining consists of major elements:

    Extract and load operation data onto the data store system.
    Store and manage the data in a multidimensional database system.
    Provide data access to business analysts and information technology professionals.
    Analyze the data by application software.
    Present the data in a useful format, such as a graph or table.

The use of data mining in business makes the data more related in application. There are several kinds of data mining: text mining, web mining, relational databases, graphic data mining, audio mining and video mining, which are all used in business intelligence applications. Data mining software is used to analyze consumer data and trends in banking as well as many other industries.


Source: http://ezinearticles.com/?Importance-of-Data-Mining-Services-in-Business&id=2601221

What You Should Know About Data Mining

Often called data or knowledge discovery, data mining is the process of analyzing data from various perspectives and summarizing it into useful information to help beef up revenue or cut costs. Data mining software is among the many analytical tools used to analyze data. It allows categorizing of data and shows a summary of the relationships identified. From a technical perspective, it is finding patterns or correlations among fields in large relational databases. Find out how data mining works and its innovations, what technological infrastructures are needed, and what tools like phone number validation can do.

Data mining may be a relatively new term, but it uses old technology. For instance, companies have made use of computers to sift through supermarket scanner data - volumes of them - and analyze years' worth of market research. These kinds of analyses help define the frequency of customer shopping, how many items are usually bought, and other information that will help the establishment increase revenue. These days, however, what makes this easy and more cost-effective are disk storage, statistical software, and computer processing power.

Data mining is mainly used by companies who want to maintain a strong customer focus, whether they're engaged in retail, finance, marketing, or communications. It enables companies to determine the different relationships among varying factors, including staffing, pricing, product positioning, market competition, and social demographics.

Data mining software, for example, vary in types: statistical, machine learning, and neural networks. It seeks any of the four types of relationships: classes (stored data is used for locating data in predetermined groups), clusters (data are grouped according to logical relationships or consumer preferences), associations (data is mined to identify associations), and sequential patterns (data is mined to estimate behavioral trends and patterns). There are different levels of analysis, including artificial neural networks, genetic algorithms, decision trees, nearest neighbor method, rule induction, and data visualization.

In today's world, data mining applications are available on all size systems from client/server, mainframe, and PC platforms. When it comes to enterprise-wide applications, the size usually ranges from 10 gigabytes to more than 11 terabytes. The two important technological drivers are the size of the database and query complexity. A more powerful system is required with more data being processed and maintained, and with more complex and greater queries.

Programmable XML web services like phone number validation will assist your company in improving the quality of your data needed for data mining. Used to validate phone numbers, a phone number validation service allows you to improve the quality of your contact database by eliminating invalid telephone numbers at the point of entry. Upon verification, phone number and other customer information can work wonders for your business and its constant improvement.


Source: http://ezinearticles.com/?What-You-Should-Know-About-Data-Mining&id=6916646

Backtesting & Data Mining

In this article we'll take a look at two related practices that are widely used by traders called Backtesting and Data Mining. These are techniques that are powerful and valuable if we use them correctly, however traders often misuse them. Therefore, we'll also explore two common pitfalls of these techniques, known as the multiple hypothesis problem and overfitting and how to overcome these pitfalls.

Backtesting

Backtesting is just the process of using historical data to test the performance of some trading strategy. Backtesting generally starts with a strategy that we would like to test, for instance buying GBP/USD when it crosses above the 20-day moving average and selling when it crosses below that average. Now we could test that strategy by watching what the market does going forward, but that would take a long time. This is why we use historical data that is already available.

"But wait, wait!" I hear you say. "Couldn't you cheat or at least be biased because you already know what happened in the past?" That's definitely a concern, so a valid backtest will be one in which we aren't familiar with the historical data. We can accomplish this by choosing random time periods or by choosing many different time periods in which to conduct the test.

Now I can hear another group of you saying, "But all that historical data just sitting there waiting to be analyzed is tempting isn't it? Maybe there are profound secrets in that data just waiting for geeks like us to discover it. Would it be so wrong for us to examine that historical data first, to analyze it and see if we can find patterns hidden within it?" This argument is also valid, but it leads us into an area fraught with danger...the world of Data Mining

Data Mining

Data Mining involves searching through data in order to locate patterns and find possible correlations between variables. In the example above involving the 20-day moving average strategy, we just came up with that particular indicator out of the blue, but suppose we had no idea what type of strategy we wanted to test? That's when data mining comes in handy. We could search through our historical data on GBP/USD to see how the price behaved after it crossed many different moving averages. We could check price movements against many other types of indicators as well and see which ones correspond to large price movements.

The subject of data mining can be controversial because as I discussed above it seems a bit like cheating or "looking ahead" in the data. Is data mining a valid scientific technique? On the one hand the scientific method says that we're supposed to make a hypothesis first and then test it against our data, but on the other hand it seems appropriate to do some "exploration" of the data first in order to suggest a hypothesis. So which is right? We can look at the steps in the Scientific Method for a clue to the source of the confusion. The process in general looks like this:

Observation (data) >>> Hypothesis >>> Prediction >>> Experiment (data)

Notice that we can deal with data during both the Observation and Experiment stages. So both views are right. We must use data in order to create a sensible hypothesis, but we also test that hypothesis using data. The trick is simply to make sure that the two sets of data are not the same! We must never test our hypothesis using the same set of data that we used to suggest our hypothesis. In other words, if you use data mining in order to come up with strategy ideas, make sure you use a different set of data to backtest those ideas.

Now we'll turn our attention to the main pitfalls of using data mining and backtesting incorrectly. The general problem is known as "over-optimization" and I prefer to break that problem down into two distinct types. These are the multiple hypothesis problem and overfitting. In a sense they are opposite ways of making the same error. The multiple hypothesis problem involves choosing many simple hypotheses while overfitting involves the creation of one very complex hypothesis.

The Multiple Hypothesis Problem

To see how this problem arises, let's go back to our example where we backtested the 20-day moving average strategy. Let's suppose that we backtest the strategy against ten years of historical market data and lo and behold guess what? The results are not very encouraging. However, being rough and tumble traders as we are, we decide not to give up so easily. What about a ten day moving average? That might work out a little better, so let's backtest it! We run another backtest and we find that the results still aren't stellar, but they're a bit better than the 20-day results. We decide to explore a little and run similar tests with 5-day and 30-day moving averages. Finally it occurs to us that we could actually just test every single moving average up to some point and see how they all perform. So we test the 2-day, 3-day, 4-day, and so on, all the way up to the 50-day moving average.

Now certainly some of these averages will perform poorly and others will perform fairly well, but there will have to be one of them which is the absolute best. For instance we may find that the 32-day moving average turned out to be the best performer during this particular ten year period. Does this mean that there is something special about the 32-day average and that we should be confident that it will perform well in the future? Unfortunately many traders assume this to be the case, and they just stop their analysis at this point, thinking that they've discovered something profound. They have fallen into the "Multiple Hypothesis Problem" pitfall.

The problem is that there is nothing at all unusual or significant about the fact that some average turned out to be the best. After all, we tested almost fifty of them against the same data, so we'd expect to find a few good performers, just by chance. It doesn't mean there's anything special about the particular moving average that "won" in this case. The problem arises because we tested multiple hypotheses until we found one that worked, instead of choosing a single hypothesis and testing it.

Here's a good classic analogy. We could come up with a single hypothesis such as "Scott is great at flipping heads on a coin." From that, we could create a prediction that says, "If the hypothesis is true, Scott will be able to flip 10 heads in a row." Then we can perform a simple experiment to test that hypothesis. If I can flip 10 heads in a row it actually doesn't prove the hypothesis. However if I can't accomplish this feat it definitely disproves the hypothesis. As we do repeated experiments which fail to disprove the hypothesis, then our confidence in its truth grows.

That's the right way to do it. However, what if we had come up with 1,000 hypotheses instead of just the one about me being a good coin flipper? We could make the same hypothesis about 1,000 different people...me, Ed, Cindy, Bill, Sam, etc. Ok, now let's test our multiple hypotheses. We ask all 1000 people to flip a coin. There will probably be about 500 who flip heads. Everyone else can go home. Now we ask those 500 people to flip again, and this time about 250 will flip heads. On the third flip about 125 people flip heads, on the fourth about 63 people are left, and on the fifth flip there are about 32. These 32 people are all pretty amazing aren't they? They've all flipped five heads in a row! If we flip five more times and eliminate half the people each time on average, we will end up with 16, then 8, then 4, then 2 and finally one person left who has flipped ten heads in a row. It's Bill! Bill is a "fantabulous" flipper of coins! Or is he?

Well we really don't know, and that's the point. Bill may have won our contest out of pure chance, or he may very well be the best flipper of heads this side of the Andromeda galaxy. By the same token, we don't know if the 32-day moving average from our example above just performed well in our test by pure chance, or if there is really something special about it. But all we've done so far is to find a hypothesis, namely that the 32-day moving average strategy is profitable (or that Bill is a great coin flipper). We haven't actually tested that hypothesis yet.

So now that we understand that we haven't really discovered anything significant yet about the 32-day moving average or about Bill's ability to flip coins, the natural question to ask is what should we do next? As I mentioned above, many traders never realize that there is a next step required at all. Well, in the case of Bill you'd probably ask, "Aha, but can he flip ten heads in a row again?" In the case of the 32-day moving average, we'd want to test it again, but certainly not against the same data sample that we used to choose that hypothesis. We would choose another ten-year period and see if the strategy worked just as well. We could continue to do this experiment as many times as we wanted until our supply of new ten-year periods ran out. We refer to this as "out of sample testing", and it's the way to avoid this pitfall. There are various methods of such testing, one of which is "cross validation", but we won't get into that much detail here.

Overfitting

Overfitting is really a kind of reversal of the above problem. In the multiple hypothesis example above, we looked at many simple hypotheses and picked the one that performed best in the past. In overfitting we first look at the past and then construct a single complex hypothesis that fits well with what happened. For example if I look at the USD/JPY rate over the past 10 days, I might see that the daily closes did this:

up, up, down, up, up, up, down, down, down, up.

Got it? See the pattern? Yeah, neither do I actually. But if I wanted to use this data to suggest a hypothesis, I might come up with...

My amazing hypothesis:

If the closing price goes up twice in a row then down for one day, or if it goes down for three days in a row we should buy,

but if the closing price goes up three days in a row we should sell,

but if it goes up three days in a row and then down three days in a row we should buy.

Huh? Sounds like a whacky hypothesis right? But if we had used this strategy over the past 10 days, we would have been right on every single trade we made! The "overfitter" uses backtesting and data mining differently than the "multiple hypothesis makers" do. The "overfitter" doesn't come up with 400 different strategies to backtest. No way! The "overfitter" uses data mining tools to figure out just one strategy, no matter how complex, that would have had the best performance over the backtesting period. Will it work in the future?

Not likely, but we could always keep tweaking the model and testing the strategy in different samples (out of sample testing again) to see if our performance improves. When we stop getting performance improvements and the only thing that's rising is the complexity of our model, then we know we've crossed the line into overfitting.


Source: http://ezinearticles.com/?Backtesting-and-Data-Mining&id=341468

Data Scrapping

People who are involved in business activities might have came across a term Data Scrapping. It is a process in which data or information can be extracted from the Portable Document Format file. They are easy to use tools that can automatically arrange the data that are found in different format in the internet. These advanced tools can collect useful information's according to the need of the user. What the user needs to do is simply enter the key words or phrases and the tool will extract all the related information available from the Portable Document Format file. It is widely used to take information's from the no editable format.

The main advantage of Portable Document Format files are they protect the originality of the document when you convert the data from Word to PDF. The size of the file is reduced by compression algorithems when the file are heavier due to the graphics or the images in the content. A Portable Document Format is independent of any software or hardware for installation. It allows encryption of files which enhances the security of your contents.

Although the Portable Document Format files have many advantages,it too have many other challenges. For example, you want to access a data that you found on the internet and the author encrypted the file preventing you from printing the file, you can easily do the scrapping process. These functions are easily available on the internet and the user can choose according to their needs. Using these programs you can extract the data that u need.


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Things You Should Know about Data Mining or Data Capturing

The World Wide Web is a portal containing billions of quality information, spanning resources from around the globe. Through the years, the internet has developed into a competitive business environment which offers advertising, promotions, sales and marketing innovations that has rapidly created a following with most websites, and gave birth to online business transactions and unprecedented financial growth.

Data mining comes into the picture in quite an obscure procedure. Most companies utilize data entry level workers to edit or create listings for the items they promote or sell online. Data mining is that early stage prior to the data entry work which utilizes available resources online to gather bits and pieces of information relevant to the business or website they are categorizing.

In a certain point of view, data mining holds a great deal of importance, as the primary keeper of the quality of the items being listed by the data entry personnel as filtered through the stages under data mining and data capturing.

As mentioned earlier, data mining is a very obscure procedure. The reason for my saying this is because of the fact that certain restrictions or policies are enforced by websites or business institutions particularly on the quality of data capturing, which may seem too time-consuming, meticulous and stringent.

These methodologies are but without explanation as well. As only the most qualified resources bearing the most relevant information can be posted online. Many data mining personnel can only produce satisfactory work on the data entry levels, after enhancing the quality of output from the data mining or data capturing stage.

Data mining includes two common strategies. The first one would be a strategy based on manual labor and data checking, with the use of online or local manual tools and scripts to gather the right information. The second would be through the use of web crawlers or robots to perform the task of checking for information on various websites automatically. The second stage offers a faster method for gathering and listing information.

But often-times the procedure spit out very garbled data, often confusing personnel more than helping.

Data mining is a highly exhaustive activity, often expending more effort, time and money than other types of work. Leveling them out, local data mining is a sure fire method to gain rapid listings of information, as collected by the information miners.


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Web Data Extraction Services

Web Data Extraction from Dynamic Pages includes some of the services that may be acquired through outsourcing. It is possible to siphon information from proven websites through the use of Data Scrapping software. The information is applicable in many areas in business. It is possible to get such solutions as data collection, screen scrapping, email extractor and Web Data Mining services among others from companies providing websites such as Scrappingexpert.com.

Data mining is common as far as outsourcing business is concerned. Many companies are outsource data mining services and companies dealing with these services can earn a lot of money, especially in the growing business regarding outsourcing and general internet business. With web data extraction, you will pull data in a structured organized format. The source of the information will even be from an unstructured or semi-structured source.

In addition, it is possible to pull data which has originally been presented in a variety of formats including PDF, HTML, and test among others. The web data extraction service therefore, provides a diversity regarding the source of information. Large scale organizations have used data extraction services where they get large amounts of data on a daily basis. It is possible for you to get high accuracy of information in an efficient manner and it is also affordable.

Web data extraction services are important when it comes to collection of data and web-based information on the internet. Data collection services are very important as far as consumer research is concerned. Research is turning out to be a very vital thing among companies today. There is need for companies to adopt various strategies that will lead to fast means of data extraction, efficient extraction of data, as well as use of organized formats and flexibility.

In addition, people will prefer software that provides flexibility as far as application is concerned. In addition, there is software that can be customized according to the needs of customers, and these will play an important role in fulfilling diverse customer needs. Companies selling the particular software therefore, need to provide such features that provide excellent customer experience.

It is possible for companies to extract emails and other communications from certain sources as far as they are valid email messages. This will be done without incurring any duplicates. You will extract emails and messages from a variety of formats for the web pages, including HTML files, text files and other formats. It is possible to carry these services in a fast reliable and in an optimal output and hence, the software providing such capability is in high demand. It can help businesses and companies quickly search contacts for the people to be sent email messages.

It is also possible to use software to sort large amount of data and extract information, in an activity termed as data mining. This way, the company will realize reduced costs and saving of time and increasing return on investment. In this practice, the company will carry out Meta data extraction, scanning data, and others as well.



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Data Mining

Data Mining is defined as the extraction of required information or knowledge from large databases. This is a powerful new technology with great potential to help companies focus on the most important information in their data warehouses. The tools related to this new technology predict future trends and behaviors, allowing businesses to make proactive, knowledge-driven decisions. The automated, prospective analysis offered by data mining move beyond the analysis of past events provided by retrospective tools typical of decision support systems. Data mining tools can answer business questions that traditionally were too time consuming to resolve. This new technology takes the process of knowledge and information acquisition beyond retrospective data access and navigation to prospective and proactive information delivery.

The Technology derives its name from the similarities between searching for valuable business information in a large database and mining a mountain for a vein of valuable ore. Both processes require either sifting through an immense amount of material, or intelligently probing it to find exactly where the value resides. Data mining automates the process of finding predictive information in large databases. Data mining tools sweep through databases and identify previously hidden patterns in one step. Data mining techniques can yield the benefits of automation on existing software and hardware platforms, and can be implemented on new systems as existing platforms are upgraded and new products developed.. Powerful systems for collecting data and managing it in large databases are in place in all large and mid-range companies.

Data Mining is predicted to be amongst the top five technologies of the world that are poised for fantastic growth and development in the next five years. Data Mining today assumes importance and significance because of the increasing thrust on knowledge and information which is an essential factor in successfully running ebusiness. Data Mining cannot replace completely human analysis and interaction. But it can greatly assist human intellect to take well thought out decisions through fast computing capabilities and through pinpointing thrust areas of the concerned business.

Data Mining is considered as the new thrust area technology, the blue-eyed boy of the ebusiness world, with great scope for expansion beyond the present day horizons of the e enterprises. Data is vital to the growth of ebusiness. And getting the right data at the right time is the crux of good business sense. Growth of web enterprises is dependent solely on knowledge and information processing. Data Mining therefore has arrived on the scene at the very appropriate time , helping these enterprises to achieve a number of complex tasks that would have taken up ages but for the advent of this marvelous new technology.



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Every Business Organization Needs Data Entry Services

Data entry is the main component of any business firm. They use this to maintain records of all sorts in a properly way. Although it seems to be an easier task but this is not the scenario, the work has to be done very cautiously and efficiently by the professional as data is very crucial. Data is priceless for any organization irrespective of their size and strength. Today, huge changes in the business industry have taken place and so businesses are adopting such new advanced techniques. These high end technologies have helped the data entry services in becoming much easier and efficient than ever before. If you are seeking to this service then must be prepared to spend more for this. So hiring this service will certainly help your business towards upward growth. Well, being the owner of your business, you are the best person to judge what will be a good strategy for your business. You can either hire a professional or can hire an outside firm to assist your data entry services task.

The newer methods of data entry services have over lapped the older and traditional methods of this service. Earlier, this service was done manually and obviously in-accuracy was found much more. So, information technology enabled services have come up with the new process that has made this service highly accurate and much easier. Indeed, every business wants to deal with this service very efficiently and accurately and so many have taken this highly enabled service for their firm. Data entry services are the key aspect of any business organization and every business needs a proper system to maintain its data and records. As data is crucial aspect of any firm irrespective of specialization or size and so they are in need of such an efficient system that can undertake their task.

An in-house data entry services would be more advantageous as you can keep a watch on the task done by professional. You can look into the procedure and other stuff that they do for your business. This can be bit expensive for your business as you will have to pay more as being an employee they are eligible for bonuses, allowances and other stuffs. If you are not satisfied with this option then you can undertake the services of a third party vendor. You can hand-over your entire task of data entry to them and can relieve of getting an efficient services. This can truly relieve you of getting a better service from them as you can get your task done in the way you desire. This option has proved to be more advantageous and proficient for many businesses. Now a day's data conversion process is highly accessed by many business firms and so gaining momentum on a large scale.

Data conversion is being done without any hassle and brings more customers to buy the products. Outsourcing of data entry services has seen huge success and businesses have seen huge profits through this service. This service has proved as a cost effective business strategy for businesses and have seen huge surge in their revenue.So, it's quite obvious that hiring data entry services from a third party vendor is better for the business then why to hire an in-house professional.


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Data Mining Models - Tom's Ten Data Tips

What is a model? A model is a purposeful simplification of reality. Models can take on many forms. A built-to-scale look alike, a mathematical equation, a spreadsheet, or a person, a scene, and many other forms. In all cases, the model uses only part of reality, that's why it's a simplification. And in all cases, the way one reduces the complexity of real life, is chosen with a purpose. The purpose is to focus on particular characteristics, at the expense of losing extraneous detail.

If you ask my son, Carmen Elektra is the ultimate model. She replaces an image of women in general, and embodies a particular attractive one at that. A model for a wind tunnel, may look like the real car, at least the outside, but doesn't need an engine, brakes, real tires, etc. The purpose is to focus on aerodynamics, so this model only needs to have an identical outside shape.

Data Mining models, reduce intricate relations in data. They're a simplified representation of characteristic patterns in data. This can be for 2 reasons. Either to predict or describe mechanics, e.g. "what application form characteristics are indicative of a future default credit card applicant?". Or secondly, to give insight in complex, high dimensional patterns. An example of the latter could be a customer segmentation. Based on clustering similar patterns of database attributes one defines groups like: high income/ high spending/ need for credit, low income/ need for credit, high income/ frugal/ no need for credit, etc.

1. A Predictive Model Relies On The Future Being Like The Past

As Yogi Berra said: "Predicting is hard, especially when it's about the future". The same holds for data mining. What is commonly referred to as "predictive modeling", is in essence a classification task.

Based on the (big) assumption that the future will resemble the past, we classify future occurrences for their similarity with past cases. Then we 'predict' they will behave like past look-alikes.

2. Even A 'Purely' Predictive Model Should Always (Be) Explain(ed)

Predictive models are generally used to provide scores (likelihood to churn) or decisions (accept yes/no). Regardless, they should always be accompanied by explanations that give insight in the model. This is for two reasons:

    buy-in from business stakeholders to act on predictions is of eminent importance, and gains from understanding
    peculiarities in data do sometimes arise, and may become obvious from the model's explanation


3. It's Not About The Model, But The Results It Generates

Models are developed for a purpose. All too often, data miners fall in love with their own methodology (or algorithms). Nobody cares. Clients (not customers) who should benefit from using a model are interested in only one thing: "What's in it for me?"

Therefore, the single most important thing on a data miner's mind should be: "How do I communicate the benefits of using this model to my client?" This calls for patience, persistence, and the ability to explain in business terms how using the model will affect the company's bottom line. Practice explaining this to your grandmother, and you will come a long way towards becoming effective.

4. How Do You Measure The 'Success' Of A Model?

There are really two answers to this question. An important and simple one, and an academic and wildly complex one. What counts the most is the result in business terms. This can range from percentage of response to a direct marketing campaign, number of fraudulent claims intercepted, average sale per lead, likelihood of churn, etc.

The academic issue is how to determine the improvement a model gives over the best alternative course of business action. This turns out to be an intriguing, ill understood question. This is a frontier of future scientific study, and mathematical theory. Bias-Variance Decomposition is one of those mathematical frontiers.

5. A Model Predicts Only As Good As The Data That Go In To It

The old "Garbage In, Garbage Out" (GiGo), is hackneyed but true (unfortunately). But there is more to this topic. Across a broad range of industries, channels, products, and settings we have found a common pattern. Input (predictive) variables can be ordered from transactional to demographic. From transient and volatile to stable.

In general, transactional variables that relate to (recent) activity hold the most predictive power. Less dynamic variables, like demographics, tend to be weaker predictors. The downside is that model performance (predictive "power") on the basis of transactional and behavioral variables usually degrades faster over time. Therefore such models need to be updated or rebuilt more often.

6. Models Need To Be Monitored For Performance Degradence

It is adamant to always, always follow up model deployment by reviewing its effectiveness. Failing to do so, should be likened to driving a car with blinders on. Reckless.

To monitor how a model keeps performing over time, you check whether the prediction as generated by the model, matches the patterns of response when deployed in real life. Although no rocket science, this can be tricky to accomplish in practice.

7. Classification Accuracy Is Not A Sufficient Indicator Of Model Quality

Contrary to common belief, even among data miners, no single number of classification accuracy (R2, Gini-coefficient, lift, etc.) is valid to quantify model quality. The reason behind this has nothing to do with the model itself, but rather with the fact that a model derives its quality from being applied.

The quality of model predictions calls for at least two numbers: one number to indicate accuracy of prediction (these are commonly the only numbers supplied), and another number to reflect its generalizability. The latter indicates resilience to changing multi-variate distributions, the degree to which the model will hold up as reality changes very slowly. Hence, it's measured by the multi-variate representativeness of the input variables in the final model.

8. Exploratory Models Are As Good As the Insight They Give

There are many reasons why you want to give insight in the relations found in the data. In all cases, the purpose is to make a large amount of data and exponential number of relations palatable. You knowingly ignore detail and point to "interesting" and potentially actionable highlights.

The key here is, as Einstein pointed out already, to have a model that is as simple as possible, but not too simple. It should be as simple as possible in order to impose structure on complexity. At the same time, it shouldn't be too simple so that the image of reality becomes overly distorted.

9. Get A Decent Model Fast, Rather Than A Great One Later

In almost all business settings, it is far more important to get a reasonable model deployed quickly, instead of working to improve it. This is for three reasons:

    A working model is making money; a model under construction is not
    When a model is in place, you have a chance to "learn from experience", the same holds for even a mild improvement - is it working as expected?
    The best way to manage models is by getting agile in updating. No better practice than doing it... :)


10. Data Mining Models - What's In It For Me?

Who needs data mining models? As the world around us becomes ever more digitized, the number of possible applications abound. And as data mining software has come of age, you don't need a PhD in statistics anymore to operate such applications.

In almost every instance where data can be used to make intelligent decisions, there's a fair chance that models could help. When 40 years ago underwriters were replaced by scorecards (a particular kind of data mining model), nobody could believe that such a simple set of decision rules could be effective. Fortunes have been made by early adopters since then.


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Data Entry Services For Multinational Companies

Data entry services envelop most multinational companies and specialized trades, which include data conversion, text and image processing, catalog processing, image enrichment, image bowdlerization, and image manipulation. Many organizations gather such information through handwritten proceedings or non transferable records of some nature, however others use full automation technical method to capture and deal with the information. For the former corporate, data entry transactions such as overhead expense hours, accounting entries or expenditure checks are items that work exceptionally fine for the dealings to be entered one at a time into an accounting software package. After being entered, the software generally manipulates the data into the accurate reports.

In earlier times, outsourcing was considered to be as a transistor alternative of meeting particular intentions; it is at present fetching the top commerce option. Viewed as a temporary business resolution, outsourcing is now a tactically imperative corporate verdict. Outsourcing your services will reduce your expenditures with improved services. Getting the profits of outsourcing data entry services for your business will be a wise preference. Numerous offshore corporations guarantee speedy and accurate data entry services.

These corporations offer entry solutions from trade specialist professionals and flexibility as per user necessities. All new reports say, development of outsourcing small priority work will persist to get bigger gradually. For those genuinely interested in creating a person on the World Wide Web by managing their data entry effectively, it is best recommended that they seek the aid of a well established professional or firm which will be well capable of delivering the best of results in a formidable manner. There are a number of data entry experts around which can help webmasters take care of their requirements, however opting for the ideal one can become a difficult task. It is therefore essential to lookout for those which have a number of positive reviews accredited to their name.


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Time Saving and Money Saving Data Entry Services

If you have an organization than data-entry is definitely the section with which you have to deal. The main concern for any organization which hires data entry services is flexibility and value for money. People need services which provide fast accurate entry of any form of hand-written data.

Data entry is very straight forward work but requires enough man force. As a result, many companies prefer to outsource data entry services to offshore countries. Company just have to find reliable data-entry partner from offshore countries which provides accurate data-entry services at most affordable prices.

As competition grows, many data-entry firms from offshore countries gives the most competitive prices for data-entry services. Outsourcing is not a new concept and having vast market doing outsourcing work. If you are looking for outsourcing data-entry work than India is the best outsourcing destination.

Many firms in India has enough experience with data entry projects which gives the best possible data-entry solutions from advanced data-entry tools. Daily, number of companies wants to move their paper documents into electronic format. All these firms in offshore countries give data entry services from qualified and well trained data-entry professionals. Their experienced and professional team of data-entry is highly trained in handling and obtaining large quantities of data in the minimal time possible. Outsourcing data entry and document processing work will save your valuable time and money. Utilizing this time and money you will be able to concentrate on your more important parts of your business leads you to high profit in best time.

Effective policies leads business to continue progress and survive them in today's highly competitive market. As in many cases, non-core activities are creating headaches in the path of progress, it is also an essential to finish them accurately as they provide assistance to core business.

So with choosing outsourcing less important data-entry work as a business strategy, allow you to create more attention on your core business activities.


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