The role of fuzzy sets in data mining

Lior Rokach

doi:10.1007/978-0-387-69935-6_8

The role of fuzzy sets in data mining

Lior Rokach

Department of Software and Information Systems Engineering

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

Abstract

In this chapter we discuss how fuzzy logic extends the envelop of the main data mining tasks: clustering, classification, regression and association rules. We begin by presenting a formulation of the data mining using fuzzy logic attributes. Then, for each task, we provide a survey of the main algorithms and a detailed description (i.e. pseudo-code) of the most popular algorithms. However this chapter will not profoundly discuss neuro-fuzzy techniques, assuming that there will be a dedicated chapter for this issue.

Original language	English
Title of host publication	Soft Computing for Knowledge Discovery and Data Mining
Publisher	Springer US
Pages	187-203
Number of pages	17
ISBN (Print)	9780387699349
DOIs	https://doi.org/10.1007/978-0-387-69935-6_8
State	Published - 1 Dec 2008

ASJC Scopus subject areas

Computer Science (all)

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10.1007/978-0-387-69935-6_8

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The role of fuzzy sets in data mining

Abstract

ASJC Scopus subject areas

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