Kernel-Based Methods to Identify Overlapping Clusters with Linear and Nonlinear Boundaries
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- 作者：Chiheb-Eddine Ben N’Cir ; Nadia Essoussi ; Mohamed Limam
- 关键词：Overlapping clustering ; Non ; disjoint clusters ; Learning multi ; labels ; Kernel methods ; Kernel K ; means ; Nonlinear separations ; Non ; linearly ; separable clusters.
- 刊名：Journal of Classification
- 出版年：2015
- 出版时间：July 2015
- 年：2015
- 卷：32
- 期：2
- 页码：176-211
- 全文大小：3,902 KB
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Nadia Essoussi (3) (4)
Mohamed Limam (1) (5)
1. LARODEC, ISG, University of Tunis, Tunis, Tunisia
2. Supérieur de Gestion de Tunis, 41 rue de la liberté, Cité Bouchoucha, 2000, Le Bardo, Tunisie
3. LARODEC, FSEG, Nabeul, Tunisia
4. University of Carthage, Carthage, Tunisia
5. Dhofar University, Salalah, Oman - 刊物类别：Mathematics and Statistics
- 刊物主题：Statistics
Statistical Theory and Methods
Pattern Recognition
Bioinformatics
Signal,Image and Speech Processing
Psychometrics
Marketing - 出版者：Springer New York
- ISSN：1432-1343
文摘
Detecting overlapping structures and identifying non-linearly-separable clusters with complex shapes are two major issues in clustering. This paper presents two kernel based methods that produce overlapping clusters with both linear and nonlinear boundaries. To improve separability of input patterns, we used for both methods Mercer kernel technique. First, we propose Kernel Overlapping K-means I (KOKMI), a centroid based method, generalizing kernel K-means to produce nondisjoint clusters with nonlinear separations. Second, we propose Kernel Overlapping K-means II (KOKMII), a medoid based method improving the previous method in terms of efficiency and complexity. Experiments performed on non-linearly-separable and real multi-labeled data sets show that proposed learning methods outperform the existing ones.