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.
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© 2006 Springer-Verlag Berlin Heidelberg
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Ling, P., Wang, Y., Zhou, CG. (2006). Self-adaptive Two-Phase Support Vector Clustering for Multi-Relational Data Mining. In: Ng, WK., Kitsuregawa, M., Li, J., Chang, K. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2006. Lecture Notes in Computer Science(), vol 3918. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11731139_27
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DOI: https://doi.org/10.1007/11731139_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-33206-0
Online ISBN: 978-3-540-33207-7
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