Data mining

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Data Mining, also known as Knowledge-Discovery in Databases (KDD), is the process of automatically searching large volumes of data for patterns. Data Mining is a fairly recent and contemporary topic in computing. However, Data Mining applies many older computational techniques from statistics, machine learning and pattern recognition.

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Definition

Data Mining can be defined as "The nontrivial extraction of implicit, previously unknown, and potentially useful information from data" Template:Ref and "The science of extracting useful information from large data sets or databases" Template:Ref. 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. It is usually associated with a business or other organization's need to identify trends.

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 may begin direct mail marketing of silk shirts to that customer, or it may alternatively attempt to get the customer to buy a wider range of products. 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. Another widely used (though hypothetical) example is that of a very large North American chain of supermarkets. Through intensive analysis of the transactions and the goods bought over a period of time, analysts found that beers and diapers were often bought together. Though explaining this interrelation might be difficult, taking advantage of it, on the other hand, should not be hard (e.g. placing the high-profit diapers next to the high-profit beers). This technique is often referred to as Market Basket Analysis.

In statistical analyses, in which there is no underlying theoretical model, data mining is often approximated via stepwise regression methods wherein the space of 2k possible relationships between a single outcome variable and k potential explanatory variables is smartly searched. With the advent of parallel computing, it became possible (when k is less than approximately 40) to examine all 2k models. This procedure is called all subsets or exhaustive regression. Some of the first applications of exhaustive regression involved the study of plant data.Template:Ref

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Data Dredging

Used in the technical context of data warehousing and analysis, the term "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. Another term for this misuse of statistics is data fishing.

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.

One common approach to evaluating the fitness of a model generated via data mining techniques is called cross validation. Cross validation is a technique that produces an estimate of generalization error based on resampling. In simple terms, the general idea behind cross validation is that dividing the data into two or or more separate data subsets allows one subset to be used to evaluate the generalizeability of the model learned from the other data subset(s). A data subset used to build a model is called a training set; the evaluation data subset is called the test set. Common cross validation techniques include the holdout method, k-fold cross validation, and the leave-one-out method.

Another pitfall of using data mining is that it may lead to discovering correlations that may not exist. "There have always been a considerable number of 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." Template:Ref. However, when properly done, determining correlations in investment analysis has proven to be very profitable for statistical arbitrage operations (such as pairs trading strategies), and furthermore correlation analysis has shown to be very useful in risk management. Indeed, finding correlations in the financial markets, when done properly, is not the same as finding false patterns in roulette wheels.

Most data mining efforts are focused on developing a finely-grained, highly detailed model of some large data set. Other researchers have described 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. Template:Ref

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Privacy concerns

There are also privacy concerns associated with data mining - specifically regarding the source of the data analyzed. For example, if an employer has access to medical records, they may screen out people who have 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. Template:Ref

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 exhibiting harmful interactions. Since any particular combination may occur in only 1 out of 1000 people, a great deal of data would need to be examined to discover such an interaction. 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.

Essentially, data mining gives information that would not 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.

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Combinatorial game data mining

Since the early 1990s, 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.

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Notable Uses of Data Mining

  • Data mining has been cited as the method by which the U.S. Army unit Able Danger supposedly had identified the 9/11 attack leader, Mohamed Atta, and three other 9/11 hijackers as possible members of an al Qaeda cell operating in the U.S. more than a year before the attack.
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See also

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References

Endnotes
  1. Template:Note W. Frawley and G. Piatetsky-Shapiro and C. Matheus, Knowledge Discovery in Databases: An Overview. AI Magazine, Fall 1992, pp. 213-228.
  1. Template:Note D. Hand, H. Mannila, P. Smyth: Principles of Data Mining. MIT Press, Cambridge, MA, 2001. ISBN 0-262-08290-X
  1. Template:Note Fred Schwed, Jr, Where Are the Customers' Yachts? ISBN 0471119792 (1940).
  1. Template:Note T. Menzies, Y. Hu, Data Mining For Very Busy People. IEEE Computer, October 2003, pp. 18-25.
  1. Template:Note 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).
  1. Template:Note A.G. Ivakhnenko, Heuristic Self-Organization in Problems of Engineering Cybernetics, GMDH library, Automatica, 6, 1970, pp.207–219.
Other
  • Pang-Ning Tan, Michael Steinbach and Vipin Kumar, Introduction to Data Mining (2005), ISBN 0-321-32136-7
  • Will Dwinnell Modeling Methodology 4: Localizing Global Models (1998) PC AI May/Jun, 1998 Using fuzzy clustering to enhance neural network performance
  • Rakesh Agrawal, Tomasz Imielinski, and Arun Swami. Mining Association Rules between Sets of Items in Large Databases (1993). Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, months 26–28, pp.207–216.
  • Rakesh Agrawal and Ramakrishnan Srikant. Fast Algorithms for Mining Association Rules (1994). Proceedings of the 20th International Conference on Very Large Data Bases (VLDB), month 12–15, pp.487–499.
  • Jaiwei Han and Micheline Kamber, Data Mining: Concepts and Techniques (2001), ISBN 1-55860-489-8
  • Ruby Kennedy et al., Solving Data Mining Problems Through Pattern Recognition (1998), ISBN 0-13-095083-1
  • O. Maimon and M. Last, Knowledge Discovery and Data Mining – The Info-Fuzzy Network (IFN) Methodology, Kluwer Academic Publishers, Massive Computing Series, 2000.
  • Hari Mailvaganam, Future of Data Mining, (December 2004)
  • Sholom Weiss and Nitin Indurkhya, Predictive Data Mining (1998), ISBN 1-55860-403-0
  • Ian Witten and Eibe Frank, Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations (2000), ISBN 1-55860-552-5
  • Yike Guo and Robert Grossman, editors, "High Performance Data Mining: Scaling Algorithms, Applications and Systems", Kluwer Academic Publishers, 1999.
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External links

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Software

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