Finding Pareto-front Membership Functions in Fuzzy Data Mining

DOI

10.1080/18756891.2012.685314How to use a DOI?

Keywords

multi-objective optimization, genetic algorithm, fuzzy set, fuzzy association rules, data mining, Pareto front

Abstract

Transactions with quantitative values are commonly seen in real-world applications. Fuzzy mining algorithms have thus been developed recently to induce linguistic knowledge from quantitative databases. In fuzzy data mining, the membership functions have a critical influence on the final mining results. How to effectively decide the membership functions in fuzzy data mining thus becomes very important. In the past, we proposed a fuzzy mining approach based on the Multi-Objective Genetic Algorithm (MOGA) to find the Pareto front of the desired membership functions. In this paper, we adopt a more sophisticated multi-objective approach, the SPEA2, to find the appropriate sets of membership functions for fuzzy data mining. Two objective functions are used to find the Pareto front. The first one is the suitability of membership functions and the second one is the total number of large 1-itemsets derived. Experimental comparisons of the proposed and the previous approaches are also made to show the effectiveness of the proposed approach in finding the Pareto-front membership functions.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Chun-Hao Chen
AU  - Tzung-Pei Hong
AU  - VincentS. Tseng
PY  - 2012
DA  - 2012/04/01
TI  - Finding Pareto-front Membership Functions in Fuzzy Data Mining
JO  - International Journal of Computational Intelligence Systems
SP  - 343
EP  - 354
VL  - 5
IS  - 2
SN  - 1875-6883
UR  - https://doi.org/10.1080/18756891.2012.685314
DO  - 10.1080/18756891.2012.685314
ID  - Chen2012
ER  -