Critical Parameters for Fuzzy Data Mining

Critical Parameters for Fuzzy Data Mining

Sinchan Bhattacharya (Maharaja Agrasen Institute of Technology, India) and Vishal Bhatnagar (Ambedkar Institute of Advanced, India)
Copyright: © 2013 |Pages: 18
DOI: 10.4018/978-1-4666-4213-3.ch011
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Abstract

Research on data mining is increasing at an incessant rate and to improve its effectiveness other techniques have been applied such as fuzzy sets, rough set theory, knowledge representation, inductive logic programming, or high-performance computing. Fuzzy logic due to its proficiency in handling uncertainty has gained its importance in a variety of applications in combination with the use of data mining techniques. In this chapter we take this association a notch further by examining the parameters which allow fuzzy sets and data mining to be combined into what has come to be known as fuzzy data mining. Analyzing and understanding these critical parameters is the main purpose of this chapter, so as to acquire maximum efficiency in applying the same which impelled the authors to work extensively and find out the crucial parameters essential to the application of fuzzy data mining.
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Background

The concept of data mining was introduced in the late 1980’s and since then it has been a blooming field of research. In his work, Kruse et al. (1999) defined, data mining as a set of tasks (Fayyad et al., 1996; Nakhaeizadeh, 1998) which include segmentation, classification, concept description, prediction, deviation analysis, and dependency analysis.

The foundations of fuzzy logic were laid in the year 1965 by Lofti, A. Zadeh. He stated that, “In a narrow sense, fuzzy logic is a logical system which is an extension of multivalued logic and is intended to serve as logic of approximate reasoning but in a wider sense, fuzzy logic is more or less synonymous with the theory of fuzzy sets, that is, a theory of classes with unsharp boundaries” (Zadeh, 1994).

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