A cluster-based divide-and-conquer genetic-fuzzy mining approach for items with multiple minimum supports

Chun Hao Chen*, Lien Chin Chen, Tzung Pei Hong, S. Tseng

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, an enhanced efficient approach for speeding up the evolution process for finding minimum supports, membership functions and fuzzy association rules is proposed by utilizing clustering techniques. All the chromosomes use the requirement satisfaction derived only from the representative chromosomes in the clusters and from their own suitability of membership functions to calculate the fitness values. The evaluation cost can thus be greatly reduced due to the cluster-based time-saving process. The final best minimum supports and membership functions in all the populations are then gathered together for mining fuzzy association rules. Experimental results also show the efficiency of the proposed approach.

Original languageEnglish
Title of host publicationProceedings - International Conference on Technologies and Applications of Artificial Intelligence, TAAI 2010
Pages532-536
Number of pages5
DOIs
StatePublished - 1 Dec 2010
Event2010 15th Conference on Technologies and Applications of Artificial Intelligence, TAAI 2010 - Hsinchu, Taiwan
Duration: 18 Nov 201020 Nov 2010

Publication series

NameProceedings - International Conference on Technologies and Applications of Artificial Intelligence, TAAI 2010

Conference

Conference2010 15th Conference on Technologies and Applications of Artificial Intelligence, TAAI 2010
CountryTaiwan
CityHsinchu
Period18/11/1020/11/10

Keywords

  • Data mining
  • Genetic algorithm
  • Genetic-fuzzy mining
  • Membership functions
  • Multiple minimum supports

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    Chen, C. H., Chen, L. C., Hong, T. P., & Tseng, S. (2010). A cluster-based divide-and-conquer genetic-fuzzy mining approach for items with multiple minimum supports. In Proceedings - International Conference on Technologies and Applications of Artificial Intelligence, TAAI 2010 (pp. 532-536). [5695504] (Proceedings - International Conference on Technologies and Applications of Artificial Intelligence, TAAI 2010). https://doi.org/10.1109/TAAI.2010.89