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2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology
Graph-Cut Based Iterative Constrained Clustering
Lyon, France
August 22-August 27
ISBN: 978-0-7695-4513-4
This paper proposes a constrained clustering method that is based on a graph-cut problem formalized by SDP (Semi-Definite Programming). Our SDP approach has the advantage of convenient constraint utilization compared with conventional spectral clustering methods. The algorithm starts from a single cluster of a complete dataset and repeatedly selects the largest cluster, which it then divides into two clusters by swapping rows and columns of a relational label matrix obtained by solving the maximum graph-cut problem. This swapping procedure is effective because we can create clusters without any computationally heavy matrix decomposition process to obtain a cluster label for each data. The results of experiments using a Web document dataset demonstrated that our method outperformed other conventional and the state of the art clustering methods in many cases. Hence we consider our clustering provides a promising basic method to interactive Web clustering.
Index Terms:
constrained clustering, semidefinite programming, graph cut
Citation:
Masayuki Okabe, Seiji Yamada, "Graph-Cut Based Iterative Constrained Clustering," wi-iat, vol. 3, pp.126-129, 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology, 2011
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