A genetic fuzzy k-Modes algorithm for clustering categorical data
The fuzzy k-Modes algorithm introduced by Huang and Ng [Huang, Z., & Ng, M. (1999). A fuzzy k-modes algorithm for clustering categorical data. IEEE Transactions on Fuzzy Systems, 7(4), 446–452] is very effective for identifying cluster structures from categorical data sets. However, the algorith...
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| Published in | Expert systems with applications Vol. 36; no. 2; pp. 1615 - 1620 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
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Elsevier Ltd
01.03.2009
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0957-4174 1873-6793 |
| DOI | 10.1016/j.eswa.2007.11.045 |
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| Abstract | The fuzzy k-Modes algorithm introduced by Huang and Ng [Huang, Z., & Ng, M. (1999). A fuzzy k-modes algorithm for clustering categorical data. IEEE Transactions on Fuzzy Systems, 7(4), 446–452] is very effective for identifying cluster structures from categorical data sets. However, the algorithm may stop at locally optimal solutions. In order to search for appropriate fuzzy membership matrices which can minimize the fuzzy objective function, we present a hybrid genetic fuzzy k-Modes algorithm in this paper. To circumvent the expensive crossover operator in genetic algorithms (GAs), we hybridize GA with the fuzzy k-Modes algorithm and define the crossover operator as a one-step fuzzy k-Modes algorithm. Experiments on two real data sets are carried out to illustrate the performance of the proposed algorithm. |
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| AbstractList | The fuzzy k-Modes algorithm introduced by Huang and Ng [Huang, Z., & Ng, M. (1999). A fuzzy k-modes algorithm for clustering categorical data. IEEE Transactions on Fuzzy Systems, 7(4), 446-452] is very effective for identifying cluster structures from categorical data sets. However, the algorithm may stop at locally optimal solutions. In order to search for appropriate fuzzy membership matrices which can minimize the fuzzy objective function, we present a hybrid genetic fuzzy k-Modes algorithm in this paper. To circumvent the expensive crossover operator in genetic algorithms (GAs), we hybridize GA with the fuzzy k-Modes algorithm and define the crossover operator as a one-step fuzzy k-Modes algorithm. Experiments on two real data sets are carried out to illustrate the performance of the proposed algorithm. |
| Author | Gan, G. Wu, J. Yang, Z. |
| Author_xml | – sequence: 1 givenname: G. surname: Gan fullname: Gan, G. email: gjgan@mathstat.yorku.ca – sequence: 2 givenname: J. surname: Wu fullname: Wu, J. email: wujh@mathstat.yorku.ca – sequence: 3 givenname: Z. surname: Yang fullname: Yang, Z. email: zyang@mathstat.yorku.ca |
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| Cites_doi | 10.1109/91.784206 10.2307/2981629 10.1007/BF01908075 10.1145/331499.331504 10.1016/S0031-3203(02)00021-3 10.1109/3477.764879 10.1016/S0019-9958(69)90591-9 10.1016/S0898-1221(99)00090-5 10.1109/ICEC.1996.542690 10.1093/comjnl/26.4.354 10.2307/2344237 10.1080/01969727308546046 |
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