Simply put, data mining is the process of extracting hidden patterns from the organization's database. Over the years it has become a more and more important tool for adding value to the company's databases. Applications include business, medicine, science and engineering, and combating terrorism. This technique actually involves two very different processes, knowledge discovery and prediction. Knowledge discovery provides users with explicit information that in a sense is sitting in the database but has not been exposed. Prediction is an attempt to read into the future.
Data mining relies on the use of real-world data. To understand how this technology works we need first to review some basic concepts. Data are any facts whether numeric or textual that can be processed by a computer. The categories include operational, non-operational, and metadata. Operational or transactional elements include accounting, cost, inventory, and sales facts and figures. Non-operational elements include forecasts and information describing competitors and the industry as a whole. Metadata describes the data itself; it is required to set up and run the databases.
Data mining commonly performs four interrelated tasks: association rule learning, classification, clustering, and regression. Let's examine each in turn. Association rule learning, also known as market basket analysis, searches for relationships between variables. A classic example is a supermarket determining which products customers buy together. Customers who buy onions and potatoes often buy beef. Classification arranges data into predefined groups. This technology can do so in a sophisticated manner. In a related technique known as clustering the groups are not predefined. Regression involves data modeling.
It has been alleged that data mining has been used both in the United States and elsewhere to combat terrorism. As always in such cases, those who know don't say, and those who say don't know. One may surmise that these anti-terrorist applications look for unusual patterns. Many credit card holders have been contacted when their spending patterns changed substantially.
Data mining has become an important feature in many customer relationship management applications. For example, this technology enables companies to focus their marketing efforts on likely customers rather than trying to sell to everyone out there. Human resources applications help companies recruit and manage employees. We have already mentioned market basket analysis. Strategic enterprise management applications help a company transform corporate targets and goals into operational decisions such as hiring and factory scheduling.
Given its great power, many people are concerned with the human rights and privacy issues around data mining. Sophisticated applications could work its way around privacy safeguards. As the technology becomes more widespread and less expensive, these issues may become more urgent. As data is summarized the wrong conclusions can be drawn. This problem not only affects human rights but also the company's bottom line.
Levi Reiss has authored or co-authored ten books on computers and the Internet. He teaches Linux and Windows operating systems plus other computer courses at an Ontario French-language community college. Visit his new website [http://www.mysql4windows.com] which teaches you how to download and run MySQL on Windows computers, even if they are "obsolete." For a break from computers check out his global wine website at http://www.theworldwidewine.com with his new weekly column reviewing $10 wines.
Source: http://ezinearticles.com/?What-You-Need-to-Know-About-Popular-Software---Data-Mining-Software&id=1920655
Data mining relies on the use of real-world data. To understand how this technology works we need first to review some basic concepts. Data are any facts whether numeric or textual that can be processed by a computer. The categories include operational, non-operational, and metadata. Operational or transactional elements include accounting, cost, inventory, and sales facts and figures. Non-operational elements include forecasts and information describing competitors and the industry as a whole. Metadata describes the data itself; it is required to set up and run the databases.
Data mining commonly performs four interrelated tasks: association rule learning, classification, clustering, and regression. Let's examine each in turn. Association rule learning, also known as market basket analysis, searches for relationships between variables. A classic example is a supermarket determining which products customers buy together. Customers who buy onions and potatoes often buy beef. Classification arranges data into predefined groups. This technology can do so in a sophisticated manner. In a related technique known as clustering the groups are not predefined. Regression involves data modeling.
It has been alleged that data mining has been used both in the United States and elsewhere to combat terrorism. As always in such cases, those who know don't say, and those who say don't know. One may surmise that these anti-terrorist applications look for unusual patterns. Many credit card holders have been contacted when their spending patterns changed substantially.
Data mining has become an important feature in many customer relationship management applications. For example, this technology enables companies to focus their marketing efforts on likely customers rather than trying to sell to everyone out there. Human resources applications help companies recruit and manage employees. We have already mentioned market basket analysis. Strategic enterprise management applications help a company transform corporate targets and goals into operational decisions such as hiring and factory scheduling.
Given its great power, many people are concerned with the human rights and privacy issues around data mining. Sophisticated applications could work its way around privacy safeguards. As the technology becomes more widespread and less expensive, these issues may become more urgent. As data is summarized the wrong conclusions can be drawn. This problem not only affects human rights but also the company's bottom line.
Levi Reiss has authored or co-authored ten books on computers and the Internet. He teaches Linux and Windows operating systems plus other computer courses at an Ontario French-language community college. Visit his new website [http://www.mysql4windows.com] which teaches you how to download and run MySQL on Windows computers, even if they are "obsolete." For a break from computers check out his global wine website at http://www.theworldwidewine.com with his new weekly column reviewing $10 wines.
Source: http://ezinearticles.com/?What-You-Need-to-Know-About-Popular-Software---Data-Mining-Software&id=1920655
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