Abstract
Prior works have elaborated on the problem of joint clustering in the optimization and geography domains. However, prior works neither clearly specify the connected constraint in the geography domain nor propose efficient algorithms. In this paper, we formulate the joint clustering problem in which a connected constraint and the number of clusters should be specified. We propose an algorithm K-means with Local Search (abbreviated as KLS) to solve the joint clustering problem with the connected constraint. Experimental results show that KLS can find correct clusters efficiently.
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Lo, CH., Peng, WC. (2008). Efficient Joint Clustering Algorithms in Optimization and Geography Domains. In: Washio, T., Suzuki, E., Ting, K.M., Inokuchi, A. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2008. Lecture Notes in Computer Science(), vol 5012. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68125-0_97
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DOI: https://doi.org/10.1007/978-3-540-68125-0_97
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-68124-3
Online ISBN: 978-3-540-68125-0
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