support vector machines, genetic algorithms, classification and regression trees, Bayesian networks, and hidden Markov models, to make better use of this explosion of information.
While there has been some overrepresentation of the gains in certain applications, these techniques have enjoyed impressive successes in many different areas.2 Data mining and related analytical tools are now used extensively to expand existing business and identify new business opportunities, to identify and prevent customer churn, to identify prospective customers, to spot trends and patterns for managing supply and demand, to identify communications and information systems faults, and to optimize business operations and performance. Some specific examples include:
In image classification, SKICAT outperformed humans and traditional computational techniques in classifying images from sky surveys comprising 3 terabytes (1012 bytes) of image data.
In marketing, American Express reported a 10-15 percent increase in credit card use through the application of marketing using data mining techniques.
In investment, LBS Capital Management uses expert systems, neural nets, and genetic algorithms to manage portfolios totaling $600 million, outperforming the broad stock market.
In fraud detection, PRISM systems are used for monitoring credit card fraud; more generally, data mining techniques have been dramatically successful in preventing billions of dollars of losses from credit card and telecommunications fraud.
In manufacturing, CASSIOPEE diagnosed and predicted problems for the Boeing 737, receiving the European first prize for innovative application.
In telecommunications, TASA uses a novel framework for locating frequently occurring alarm episodes from the alarm stream, improving the ability to prune, group, and develop new rules.
In the area of data cleaning, the MERGE-PURGE system was successfully applied to the identification of welfare claims for the State of Washington.
In the area of Internet search, data mining tools have been used to improve search tools that assist in locating items of interest based on a user profile.
Under their broadest definitions, data mining techniques include a