Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Comments on “A possibilistic approach to clustering”
IEEE Transactions on Fuzzy Systems
Improved possibilistic C-means clustering algorithms
IEEE Transactions on Fuzzy Systems
Mercer kernel-based clustering in feature space
IEEE Transactions on Neural Networks
Possibilistic clustering using non-Euclidean distance
CCDC'09 Proceedings of the 21st annual international conference on Chinese Control and Decision Conference
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A novel fuzzy clustering algorithm, called kernel improved possibilistic c-means (KIPCM) algorithm, is presented based on kernel methods. KIPCM is an extension of the improved possibilistic c-means (IPCM) algorithm. Different from IPCM which is applied in Euclidean space, KIPCM can make data clustering in kernel feature space. With kernel methods the input data can be implicitly mapped into a high-dimensional feature space where the nonlinear pattern now appears linear. It is unnecessary to calculate in this high-dimensional feature space because we directly calculate inner products from the input data by kernel function. KIPCM can identify clusters of complex shapes and solve nonlinear separable problems better than IPCM and FCM (fuzzy c-means). Our experiments show that the proposed algorithm compares favorably with FCM and IPCM.