An improved differential evolution and its application to determining feature weights in similarity-based clustering
Autor: | Wing W. Y. Ng, Patrick P. K. Chan, Chun-Ru Dong, Daniel S. Yeung, Xizhao Wang |
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Rok vydání: | 2014 |
Předmět: |
Fuzzy clustering
business.industry Cognitive Neuroscience Correlation clustering Pattern recognition Computer Science Applications Similarity (network science) Artificial Intelligence Feature (computer vision) Differential evolution FLAME clustering Artificial intelligence Cluster analysis business Gradient descent Mathematics |
Zdroj: | Neurocomputing. 146:95-103 |
ISSN: | 0925-2312 |
Popis: | 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. |
Databáze: | OpenAIRE |
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