An Efficient and Robust Kernelized Possibilistic C-Means Clustering Algorithm

Abstract:

To avoid the initialization sensitivity and low computational efficiency problems of the kernelized possibilistic c-means clustering algorithm (KPCM), a new clustering algorithm called efficient and robust kernelized possibilistic c-means clustering algorithm (ERKPCM) was proposed in this paper. ERKPCM improved KPCM by two ways. First, the data are refined by the data reduction technique, which makes it keep the data structure of the original data and have higher efficiency. Secondly, weighted clustering algorithm is executed several times to estimate cluster centers accurately, which makes it more robust to initializations and get more reasonable partitions. As a by-product, ERKPCM overcomes the problem of generating coincident clusters of KPCM. The contrast experimental results with conventional algorithms show that ERKPCM is more robust to initializations, and has a relatively high precision and efficiency.

• Periodical: Advanced Materials Research (Volumes 962-965)
• Pages: 2881-2885
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.962-965.2881
• Online since: June 2014
• Authors: Ya Ting Hu, Fu Heng Qu*, Yao Hong Xue, Yong Yang
• Keywords: Data Reduction, Initialization Sensitivity, Kernel Function, Possibilistic C-Means Clustering

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