Finding Active Membership Functions for Genetic-Fuzzy Data Mining

Chun-Hao Chen (), Tzung-Pei Hong, Yeong-Chyi Lee () and Vincent S. Tseng ()
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Chun-Hao Chen: Department of Computer Science and Information Engineering, Tamkang University, Taipei 251, Taiwan, R.O.C.
Tzung-Pei Hong: Department of Computer Science and Information Engineering National University of Kaohsiung, Kaohsiung 811, Taiwan, R.O.C.3Department of Computer Science and Engineering National Sun Yat-Sen University, Kaohsiung 804, Taiwan, R.O.C.
Yeong-Chyi Lee: Department of Information Management Cheng Shiu University, Kaohsiung 833, Taiwan, R.O.C.
Vincent S. Tseng: Department of Computer Science and Information Engineering National Cheng Kung University, Tainan 701, Taiwan, R.O.C.

International Journal of Information Technology & Decision Making (IJITDM), 2015, vol. 14, issue 06, 1215-1242

Abstract: Since transactions may contain quantitative values, many approaches have been proposed to derive membership functions for mining fuzzy association rules using genetic algorithms (GAs), a process known as genetic-fuzzy data mining. However, existing approaches assume that the number of linguistic terms is predefined. Thus, this study proposes a genetic-fuzzy mining approach for extracting an appropriate number of linguistic terms and their membership functions used in fuzzy data mining for the given items. The proposed algorithm adjusts membership functions using GAs and then uses them to fuzzify the quantitative transactions. Each individual in the population represents a possible set of membership functions for the items and is divided into two parts, control genes (CGs) and parametric genes (PGs). CGs are encoded into binary strings and used to determine whether membership functions are active. Each set of membership functions for an item is encoded as PGs with real-number schema. In addition, seven fitness functions are proposed, each of which is used to evaluate the goodness of the obtained membership functions and used as the evolutionary criteria in GA. After the GA process terminates, a better set of association rules with a suitable set of membership functions is obtained. Experiments are made to show the effectiveness of the proposed approach.

Keywords: Data mining; fuzzy sets; fuzzy association rules; genetic algorithms; membership functions (search for similar items in EconPapers)
Date: 2015
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DOI: 10.1142/S0219622015500297

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