NDLI: A modified approach to speed up genetic-fuzzy data mining with divide-and-conquer strategy

Content Provider	IEEE Xplore Digital Library
Author	Chun-Hao Chen Tzung-Pei Hong Tseng, V.S.
Copyright Year	2007
Description	Author affiliation: Nat. Cheng-Kung Univ., Tainan (Chun-Hao Chen)
Abstract	In the past, we proposed a fuzzy data-mining algorithm for extracting both association rules and membership functions from quantitative transactions based on the divide-and-conquer strategy. In this paper, an enhanced approach, called the cluster-based genetic-fuzzy mining algorithm, is thus proposed to speed up the evaluation process and keep nearly the same quality of solutions as the previous one. It first divides the chromosomes in a population into k clusters by the A-means clustering approach and evaluates each individual according to its own information and the information of the cluster it belongs to. The final best sets of membership functions in all the populations are then gathered together for mining fuzzy association rules. Experimental results also show the effectiveness and efficiency of the proposed approach.
Starting Page	1
Ending Page	6
File Size	301989
Page Count	6
File Format	PDF
ISBN	9781424413393
DOI	10.1109/CEC.2007.4424447
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-09-25
Publisher Place	Singapore
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Data mining Evolutionary computation
Content Type	Text
Resource Type	Article

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A modified approach to speed up genetic-fuzzy data mining with divide-and-conquer strategy