Genetic-fuzzy mining with multiple minimum supports based on fuzzy clustering |
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Authors: | Chun-Hao Chen Tzung-Pei Hong Vincent S Tseng |
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Affiliation: | (1) Department of Computer Science and Information Engineering, Tamkang University, Taipei, 251, Taiwan, ROC;(2) Department of Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung, 811, Taiwan, ROC;(3) Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung, 804, Taiwan, ROC;(4) Department of Computer Science and Information Engineering, National Cheng-Kung University, Tainan, 701, Taiwan, ROC |
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Abstract: | Data mining is the process of extracting desirable knowledge or interesting patterns from existing databases for specific
purposes. Most of the previous approaches set a single minimum support threshold for all the items and identify the relationships
among transactions using binary values. In real applications, different items may have different criteria to judge their importance.
In the past, we proposed an algorithm for extracting appropriate multiple minimum support values, membership functions and
fuzzy association rules from quantitative transactions. It used requirement satisfaction and suitability of membership functions
to evaluate fitness values of chromosomes. The calculation for requirement satisfaction might take a lot of time, especially
when the database to be scanned could not be totally fed into main memory. In this paper, an enhanced approach, called the
fuzzy cluster-based genetic-fuzzy mining approach for items with multiple minimum supports (FCGFMMS), is thus proposed to
speed up the evaluation process and keep nearly the same quality of solutions as the previous one. It divides the chromosomes
in a population into several clusters by the fuzzy k-means clustering approach and evaluates each individual according to both their cluster and their own information. Experimental
results also show the effectiveness and the efficiency of the proposed approach. |
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