NDLI: A Possibilistic C-Means Clustering Algorithm Based on Kernel Methods

Content Provider	IEEE Xplore Digital Library
Author	Xiao-Hong Wu
Copyright Year	2006
Description	Author affiliation: Coll. of Electr. & Inf. Eng., Jiangsu Univ., Zhenjiang (Xiao-Hong Wu)
Abstract	A novel fuzzy clustering algorithm, called kernel possibilistic c-means model (KPCM), is proposed. KPCM algorithm is based on kernel methods and possibilistic c-means (PCM) algorithm and it is the extension of PCM algorithm. Different from PCM and FCM which are based on Euclidean distance, the proposed model is based on kernel-induced distance by using kernel methods. Furthermore, with kernel methods the input data can be mapped implicitly into a high-dimensional feature space where possibilistic c-means clustering is carried out. FCM, PCM and KPCM are performed numerical experiments on data sets. The experimental results show the better performance of KPCM
Sponsorship	IEEE Circuits and Syst. Soc. HKUST UESTC City Univ. of Hong Kong
Starting Page	2062
Ending Page	2066
File Size	4021791
Page Count	5
File Format	PDF
ISBN	0780395840
DOI	10.1109/ICCCAS.2006.285084
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-06-25
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Phase change materials Algorithm design and analysis Fuzzy sets Information science Space technology Clustering algorithms Educational institutions Fuzzy set theory Partitioning algorithms Kernel
Content Type	Text
Resource Type	Article

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