Abstract: We propose a novel clustering algorithm using fast global kernel fuzzy c-means-F (FGKFCM-F), where F refers to kernelized feature space. This algorithm proceeds in an incremental way to derive the near-optimal solution by solving all intermediate problems using kernel-based fuzzy c-means-F (KFCM-F) as a local search procedure. Due to the incremental nature and the nonlinear properties inherited from KFCM-F, this algorithm overcomes the two shortcomings of fuzzy c-means (FCM): sensitivity to initialization and inability to use nonlinear separable data. An accelerating scheme is developed to reduce the computational complexity without significantly affecting the solution quality. Experiments are carried out to test the proposed algorithm on a nonlinear artificial dataset and a real-world dataset of speech signals for consonant/vowel segmentation. Simulation results demonstrate the effectiveness of the proposed algorithm in improving clustering performance on both types of datasets.

Key words: Fuzzy c-means clustering, Kernel method, Global optimization, Consonant/vowel segmentation

An erratum to this article can be found at doi:10.1631/jzus.C13e0320

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