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Lyon
Aug. 22, 2011 to Aug. 27, 2011
ISBN: 978-1-4577-1373-6
pp: 126-129
ABSTRACT
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, 2011, 2011 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies, 2011 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies 2011, pp. 126-129, doi:10.1109/WI-IAT.2011.42
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