Self-adaptive two-phase support vector clustering for multi-relational data mining

Authors:
Ping Ling;Yan Wang;Chun-Guang Zhou
Affiliations:
College of Computer Science, Jilin University, Key Laboratory of Symbol Computation, and Knowledge Engineering of the Ministry of Education, Changchun, China;College of Computer Science, Jilin University, Key Laboratory of Symbol Computation, and Knowledge Engineering of the Ministry of Education, Changchun, China;College of Computer Science, Jilin University, Key Laboratory of Symbol Computation, and Knowledge Engineering of the Ministry of Education, Changchun, China
Venue:
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Year:
2006

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Abstract

This paper proposes a novel Self-Adaptive Two-Phase Support Vector Clustering algorithm (STPSVC) to cluster multi-relational data. The algorithm produces an appreciate description of cluster contours and then extracts cluster centers information by iteratively performing classification procedure. An adaptive Kernel function is designed to find a desired width parameter for diverse dispersions. Experimental results indicate that the designed Kernel can capture multi-relational features well and STPSVC is of fine performance.