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Data mining, also known as knowledge-discovery in databases (KDD), is the practice of automatically searching large stores of data for patterns. To do this, data mining uses computational techniques from Statistics and Pattern recognition.
Data mining has been defined as "The nontrivial extraction of implicit, previously unknown, and potentially useful information from data"  and "The science of extracting useful information from large data sets or databases" . Although it is usually used in relation to analysis of data, data mining, like artificial intelligence, is an umbrella term and is used with varied meaning in a wide range of contexts.
A simple example of data mining is its use in a retail sales department. If a store tracks the purchases of a customer and notices that a customer buys a lot of silk shirts, the data mining system will make a correlation between that customer and silk shirts. The sales department will look at that information and begin direct mail marketing of silk shirts to that customer. In this case, the data mining system used by the retail store discovered new information about the customer that was previously unknown to the company.
Used in the technical context of data warehousing and analysis, data mining is neutral. However, it sometimes has a more pejorative usage that implies imposing patterns (and particularly causal relationships) on data where none exist. This imposition of irrelevant, misleading or trivial attribute correlation is more properly criticized as "data dredging" in the statistical literature.
Used in this latter sense, data dredging implies scanning the data for any relationships, and then when one is found coming up with an interesting explanation. (This is also referred to as "overfitting the model".) The problem is that large data sets invariably happen to have some exciting relationships peculiar to that data. Therefore any conclusions reached are likely to be highly suspect. In spite of this, some exploratory data work is always required in any applied statistical analysis to get a feel for the data, so sometimes the line between good statistical practice and data dredging is less than clear.
A more significant danger is finding correlations that do not really exist. Investment analysts appear to be particularly vulnerable to this. In his book Where Are the Customers' Yachts? ISBN 0471119792 (1940), Fred Schwed, Jr , wrote: "There have always been a considerable number of pathetic people who busy themselves examining the last thousand numbers which have appeared on a roulette wheel, in search of some repeating pattern. Sadly enough, they have usually found it."
Most data mining efforts are focused on developing a finely-grained, highly detailed model of some large data set. In Data Mining For Very Busy People , researchers at West Virginia University and the University of British Columbia discuss an alternate method that involves finding the minimal differences between elements in a data set, with the goal of developing simpler models that represent relevant data.
There are also privacy concerns associated with data mining. For example, if an employer has access to medical records, they may screen out people with diabetes or have had a heart attack. Screening out such employees will cut costs for insurance, but it creates ethical and legal problems.
Data mining government or commercial data sets for national security or law enforcement purposes has also raised privacy concerns. 
There are many legitimate uses of data mining. For example, a database of prescription drugs taken by a group of people could be used to find combinations of drugs with an adverse reactions. Since the combination may occur in only 1 out of 1000 people, a single case may not be apparent. A project involving pharmacies could reduce the number of drug reactions and potentially save lives. Unfortunately, there is also a huge potential for abuse of such a database.
Basically, data mining gives information that wouldn't be available otherwise. It must be properly interpreted to be useful. When the data collected involves individual people, there are many questions concerning privacy, legality, and ethics.
The a priori algorithm is the most fundamental algorithm used in data mining.
Since the early 1990's, with the availability of oracles for certain combinatorial games, also called tablebases (e.g. for 3x3-chess) with any beginning configuration, small-board dots-and-boxes, small-board-hex, and certain endgames in chess, dots-and-boxes, and hex; a new area for data mining has been opened up. This is the extraction of human-usable strategies from these oracles. This is pattern-recognition at too high an abstraction for known Statistical Pattern Recognition algorithms or any other algorithmic approaches to be applied: at least, no one knows how to do it yet (as of January 2005). The method used is the full force of Scientific Method: extensive experimentation with the tablebases combined with intensive study of tablebase-answers to well designed problems, combined with knowledge of prior art i.e. pre-tablebase knowledge, leading to flashes of insight. Berlekamp in dots-and-boxes etc. and John Nunn in chess endgames are notable examples of people doing this work, though they were not and are not involved in tablebase generation.
Data Mining grew as a direct consequence of the availability of large reservoirs of data. Data Collection in digitalized forms had already commenced in the 1960s allowing for retrospective data analysis via computers. Relational Databases arose in the 1980s along with Structured Query Languages (SQL) allowing for dynamic on-demand analysis of data. The 1990s saw an explosion in growth of data. Data warehouses were beginning to be used for storage of data. Data Mining thus arose as a response to challenges faced by the database community in dealing with massive amounts of data, application of statistical analysis to data and application of search techniques from Artificial Intelligence to these problems.
- Data warehouse
- Document warehouse
- Machine learning
- Pattern recognition
- Artificial intelligence
- Descriptive statistics
- Hypothesis testing
- Loyalty card
- Business intelligence
- Business performance management
- Text mining
- Relational data mining
- Data stream mining
 W. Frawley and G. Piatetsky-Shapiro and C. Matheus, Knowledge Discovery in Databases: An Overview. AI Magazine, Fall 1992, pgs 213-228.
 D. Hand, H. Mannila, P. Smyth: Principles of Data Mining. MIT Press, Cambridge, MA, 2001. ISBN 0-262-08290-X
 T. Menzies, Y. Hu, Data Mining For Very Busy People. IEEE Computer, October 2003, pgs 18-25.
 K. A. Taipale, Data Mining and Domestic Security: Connecting the Dots to Make Sense of Data, Center for Advanced Studies in Science and Technology Policy. 5 Colum. Sci. & Tech. L. Rev. 2 (December 2003).
 O. Maimon and M. Last, Knowledge Discovery and Data Mining – The Info-Fuzzy Network (IFN) Methodology, Kluwer Academic Publishers, Massive Computing Series, 2000.
- SearchCRM.com Original daily breaking news, white papers, expert advice, webcasts, product reviews and more on data mining.
- Limited intoduction to Data Mining (TwoCrows.com)
- Comprehensive data mining white papers and tutorials (thearling.com)
- SQLServerDataMining.com Information and interactive demos on SQL Server 2005 Data Mining
- CRM Today - Data Mining White papers, articles, presentations and academic papers on data mining
- Data Mining whitepapers, webcasts and case studies
- KDnuggets Directory for data mining, knowledge discovery, genomic mining, web mining
- Data Mining Software from Siebel
- Kmining List of data mining and KDD scientific conferences
- Data Mining Guide
- Data Mining and Data Warehousing Guide to Data Mining
- Weka Open source data mining software written in Java
- Mining Data Streams Bibliography Links to papers related to mining data streams, its techniques and applications
- IBM DB2 Universal Database Data Warehouse Editions
- Microsoft SQL Server
- Orange (Open Source/GPL)
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