title:
 
Finding Pareto-front Membership Functions in Fuzzy Data Mining
publication:
 
IJCIS
volume-issue:   5 - 2
pages:   343 - 354
ISSN:
  1875-6883
DOI:
  doi:10.2991/10.1080/18756891.2012.685314 (how to use a DOI)
author(s):
 
Chun-Hao Chen, Tzung-Pei Hong, VincentS. Tseng
publication date:
 
April 2012
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:
 
© The authors.
This article is distributed under the terms of the Creative Commons Attribution License 4.0, which permits non-commercial use, distribution and reproduction in any medium, provided the original work is properly cited. See for details: https://creativecommons.org/licenses/by-nc/4.0/
full text: