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2004

K-medoid-style Clustering Algorithms for Supervised Summary Generation

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K-medoid-style Clustering Algorithms for Supervised Summary Generation
This paper centers on the discussion of k-medoid-style clustering algorithms for supervised summary generation. This task requires clustering techniques that identify class-uniform clusters. This paper investigates such a novel clustering technique we term supervised clustering. Our work focuses on the generalization of k-medoid-style clustering algorithms. We investigate two supervised clustering algorithms: SRIDHCR (Single Representative Insertion/Deletion Hill Climbing with Restart) and SPAM, a variation of PAM. The solution quality and run time of these two algorithms as well as the traditional clustering algorithm PAM are evaluated using a benchmark consisting of four data sets. Experiments show that supervised clustering algorithms enhance class purity by 7% to 19% over the traditional clustering algorithm PAM, and that SRIDHCR finds better solutions than SPAM. Key Words: supervised summary generation, clustering classified examples, k-medoid clustering algorithms, data mining.
Nidal M. Zeidat, Christoph F. Eick
Added 31 Oct 2010
Updated 31 Oct 2010
Type Conference
Year 2004
Where ICAI
Authors Nidal M. Zeidat, Christoph F. Eick
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