A cluster-based genetic-fuzzy mining approach for items with multiple minimum supports: `

Authors:
Chun-Hao Chen;Tzung-Pei Hong;Vincent S. Tseng
Affiliations:
Department of Computer Science and Information Engineering, National Cheng Kung University, Taiwan;Department of Computer Science and Information Engineering, National University of Kaohsiung, Taiwan and Department of Computer Science and Engineering, National Sun Yat-sen University, Taiwan;Department of Computer Science and Information Engineering, National Cheng Kung University, Taiwan
Venue:
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Year:
2008

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Abstract

In the past, we proposed an algorithm for extracting appropriate multiple minimum support values, membership functions and fuzzy association rules form quantitative transactions. The evaluation process might take a lot of time, especially when the database to be scanned could not totally fed into main memory. In this paper, an enhanced approach, called the Cluster-based Genetic-Fuzzy mining approach for items with Multiple Minimum Supports (CGFMMS), is thus proposed to speed up the evaluation process and keep nearly the same quality of solutions as the previous one. Experimental results also show the effectiveness and the efficiency of the proposed approach.