NDLI: Two-phase support vector clustering for multi-relational data mining

Content Provider	IEEE Xplore Digital Library
Author	Ling Ping Wang Yan Lu Nan Wang Jian Yu Liang Shuang Zhou ChunGuang
Copyright Year	2005
Description	Author affiliation: Coll. of Comput. Sci., Jilin Univ., Changchun (Ling Ping; Wang Yan; Lu Nan; Wang Jian Yu)
Abstract	A novel two-phase support vector clustering (TPSVC) algorithm is proposed in this paper, which is implemented in multi-relational data mining (MRDM). Based on the designed kernel which is incorporated with MRDM environment, TPSVC provides an appreciate description of cluster contours using support vectors at the first step and then a support vector machine (SVM) classification procedure is employed to further extract the information of cluster central zones. The algorithm does the cluster assignment according to desired definition of affinity without suffering the expensive operations of adjacent matrix computation used in traditional support vector clustering (SVC). Experimental results indicate that the designed kernel can capture the features of relational schema and TPSVC is of fine clustering performance
Starting Page	8
Ending Page	146
File Size	290453
Page Count	139
File Format	PDF
ISBN	0769523781
DOI	10.1109/CW.2005.92
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-11-23
Publisher Place	Singapore
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Data mining Kernel Support vector machines Support vector machine classification Clustering algorithms Static VAr compensators Logic programming Computer science Probabilistic logic Educational institutions
Content Type	Text
Resource Type	Article

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