A Novel Multiple Fuzzy Clustering Method Based on Internal Clustering Validation Measures with Gradient Descent

In this paper, we propose a novel multiple fuzzy clustering method based on internal clustering validation measures with gradient descent. Firstly, some single fuzzy clustering algorithms such as Fuzzy C-Means, Kernel Fuzzy C-Means and Gustafson–Kessel are used to construct similarity matrixes for e...

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Bibliographic Details
Published in:International journal of fuzzy systems 2016-10, Vol.18 (5), p.894-903
Main Authors: Son, Le Hoang, Van Hai, Pham
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Clustering
Computational Intelligence
Data compression
Engineering
Machine learning
Management Science
Methods
Operations Research
Pattern recognition
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Summary:In this paper, we propose a novel multiple fuzzy clustering method based on internal clustering validation measures with gradient descent. Firstly, some single fuzzy clustering algorithms such as Fuzzy C-Means, Kernel Fuzzy C-Means and Gustafson–Kessel are used to construct similarity matrixes for each partition. Secondly, those similarity matrixes are aggregated into a final one by means of the direct sum of weighted vectors where the values of weights are determined by internal clustering validation measures. Finally, final membership matrix is calculated by minimizing the sum of square errors through the gradient descent method. The proposed approach has been validated in terms of clustering quality on UCI Machine Learning Repository datasets. Experimental results show that the proposed approach’s performance is better than those of other ensemble and standalone methods.
ISSN:1562-2479
2199-3211
DOI:10.1007/s40815-015-0117-1