| 基于核方法的改进可能聚类模型 |
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| 引用本文： | 武小红,周建江.基于核方法的改进可能聚类模型[J].上海大学学报(英文版),2008,12(2):136-140. |
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| 作者姓名： | 武小红 周建江 |
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| 作者单位： | [1]College of Information＇Science and Technoiogy, Nanjing University of Aeronautics and Astronaaltics, Nanjing 210016, P. R. China [2]School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, P. R. China |
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| 基金项目： | Project supported by the 15th Plan for National Defence Preventive Research Project (Grant No.413030201) |
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| 摘 要： |
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| 关 键 词： | fuzzy clustering kernel methods possibilistic c-means (PCM) kernel modified possibilistic c-means (KMPCM). 核方法 改进 聚类模型 kernel methods based model fuzzy clustering Numerical experiments show kernel function input data feature space nonlinear pattern distance Different algorithm extension |
| 收稿时间： | 2006-09-20 |
| 修稿时间： | 2006年9月20日 |
Modified possibilistic clustering model based on kernel methods |
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| Xiao-hong Wu,Jian-jiang Zhou.Modified possibilistic clustering model based on kernel methods[J].Journal of Shanghai University(English Edition),2008,12(2):136-140. |
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| Authors: | Xiao-hong Wu Jian-jiang Zhou |
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| Institution: | 1. College of Information Science and Technology, Nanjing University of Aeronautics and Astronautics,Nanjing 210016, P. R. China;School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, P. R. China
2. College of Information Science and Technology, Nanjing University of Aeronautics and Astronautics,Nanjing 210016, P. R. China |
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| Abstract: | A novel model of fuzzy clustering using kernel methods is proposed. This model is called kernel modified possibilisticc-means (KMPCM) model. The proposed model is an extension of the modified possibilistic c-means (MPCM) algorithm byusing kernel methods. Different from MPCM and fuzzy c-means (FCM) model which are based on Euclidean distance, theproposed model is based on kernel-induced distance. Furthermore, with kernel methods the input data can be mappedimplicitly into a high-dimensional feature space where the nonlinear pattern now appears linear. It is unnecessary to docalculation in the high-dimensional feature space because the kernel function can do it. Numerical experiments show thatKMPCM outperforms FCM and MPCM. |
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| Keywords: | fuzzy clustering kernel methods possibilistic c-means (PCM) kernel modified possibilistic c-means (KMPCM) |
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