期刊
NEUROCOMPUTING
卷 146, 期 -, 页码 95-103出版社
ELSEVIER
DOI: 10.1016/j.neucom.2014.04.065
关键词
Similarity-based clustering; Feature weighting; Differential evolution; Dynamic differential evolution; Differential evolution strategy
资金
- National Natural Science Foundation of China [61003171, 61272201, 61170040]
- Natural Science Foundation of Hebei Province [F2013201110, F2013201220]
- Key Scientific Research Foundation of Education Department of Hebei Province [ZD2010139]
- Development of Science and Technology Mentoring Program of Baoding [10ZG007]
In this work, we propose an optimization model to tune feature weights for improving performance of clustering via a minimization of uncertainty (fuzziness and non-specificity) of its similarity matrix among objects. To solve the proposed model efficiently, we propose an evolutionary search approach by integrating multiple strategies from both differential evolution and dynamic differential evolution. Then, the proposed method is applied to both weighted fuzzy c-means and weighted similarity-matrix-based transitive closure clustering. Experiments on 11 benchmarking databases show that the proposed method outperforms clustering methods without feature weighting and the feature weighting method based on gradient descent in terms of clustering performance evaluation indices and robustness. (C) 2014 Elsevier B.V. All rights reserved.
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