Using Fuzzy Logic in Data Mining

Lior Rokach

doi:10.1007/978-0-387-09823-4_24

Using Fuzzy Logic 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	DATA MINING AND KNOWLEDGE DISCOVERY HANDBOOK, SECOND EDITION
Publisher	Springer, Boston, MA
Pages	505-520
ISBN (Electronic)	978-0-387-09823-4
ISBN (Print)	978-0-387-09822-7
DOIs	https://doi.org/10.1007/978-0-387-09823-4_24
State	Published - 2010

Keywords

Data Mining
Membership Function
Fuzzy Logic
Association Rule
Fuzzy Rule

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10.1007/978-0-387-09823-4_24

Cite this

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KW - Membership Function

KW - Fuzzy Logic

KW - Association Rule

KW - Fuzzy Rule

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BT - DATA MINING AND KNOWLEDGE DISCOVERY HANDBOOK, SECOND EDITION

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Using Fuzzy Logic in Data Mining

Abstract

Keywords

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