• Data reduction is the most ubiquitous application: exploiting patterns in data to create a more compact
representation of the original. Though vastly broader in scope, data reduction includes analytic methods
such as cluster analysis.
• Novelty detection methods seek unique or previously unobserved data patterns. The methods find
application in business, science, and engineering. Business applications include fraud detection,
warranty claims analysis, and general business process monitoring.
• Profiling is a by-product of reduction methods such as cluster analysis. The idea is to create rules that
isolate clusters or segments, often based on demographic or behavioral measurements. A marketing
analyst might develop profiles of a customer database to describe the consumers of a company’s
• Market basket analysis, or association rule discovery, is used to analyze streams of transactions data
(for example, market baskets) for combinations of items that occur (or do not occur) more (or less)
commonly than expected. Retailers can use this as a way to identify interesting combinations of
purchases or as predictors of customer segments.
• Sequence analysis is an extension of market basket analysis to include a time dimension to the
analysis, i.e. Path analysis. In this way, transactions data is examined for sequences of items that occur (or do not occur)
more (or less) commonly than expected. A Webmaster might use sequence analysis to identify patterns
or problems of navigation through a Web site.