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Metric Methods in Data Mining

Metric Methods in Data Mining

Dan A. Simovici
Copyright: © 2008 |Pages: 31
ISBN13: 9781599049519|ISBN10: 1599049511|EISBN13: 9781599049526
DOI: 10.4018/978-1-59904-951-9.ch052
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MLA

Simovici, Dan A. "Metric Methods in Data Mining." Data Warehousing and Mining: Concepts, Methodologies, Tools, and Applications, edited by John Wang, IGI Global, 2008, pp. 849-879. https://doi.org/10.4018/978-1-59904-951-9.ch052

APA

Simovici, D. A. (2008). Metric Methods in Data Mining. In J. Wang (Ed.), Data Warehousing and Mining: Concepts, Methodologies, Tools, and Applications (pp. 849-879). IGI Global. https://doi.org/10.4018/978-1-59904-951-9.ch052

Chicago

Simovici, Dan A. "Metric Methods in Data Mining." In Data Warehousing and Mining: Concepts, Methodologies, Tools, and Applications, edited by John Wang, 849-879. Hershey, PA: IGI Global, 2008. https://doi.org/10.4018/978-1-59904-951-9.ch052

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Abstract

This chapter presents data mining techniques that make use of metrics defined on the set of partitions of finite sets. Partitions are naturally associated with object attributes and major data mining problem such as classification, clustering, and data preparation benefit from an algebraic and geometric study of the metric space of partitions. The metrics we find most useful are derived from a generalization of the entropic metric. We discuss techniques that produce smaller classifiers, allow incremental clustering of categorical data and help user to better prepare training data for constructing classifiers. Finally, we discuss open problems and future research directions.

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