A Comparison of Three Graph Partitioning Based Methods for Consensus Clustering

by: Tianming Hu, Weiquan Zhao, Xiaoqiang Wang, Zhixiong Li

Rough Sets and Knowledge Technology (2006), pp. 468-475.

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Abstract

Consensus clustering refers to combining multiple clusterings over a common dataset into a consolidated better one. This paper compares three graph partitioning based methods. They differ in how to summarize the clustering ensemble in a graph. They are evaluated in a series of experiments, where component clusterings are generated by tuning parameters controlling their quality and resolution. Finally the combination accuracy is analyzed as a function of the learning dynamics vs. the number of clusterings involved. Keywords: Consensus clustering, graph partitioning, clustering ensemble, consensus function, data mining.

@incollection{citeulike:2033149, abstract = {Consensus clustering refers to combining multiple clusterings over a common dataset into a consolidated better one. This paper compares three graph partitioning based methods. They differ in how to summarize the clustering ensemble in a graph. They are evaluated in a series of experiments, where component clusterings are generated by tuning parameters controlling their quality and resolution. Finally the combination accuracy is analyzed as a function of the learning dynamics vs. the number of clusterings involved. Keywords: Consensus clustering, graph partitioning, clustering ensemble, consensus function, data mining.}, author = {Hu, Tianming and Zhao, Weiquan and Wang, Xiaoqiang and Li, Zhixiong }, citeulike-article-id = {2033149}, doi = {http://dx.doi.org/10.1007/11795131\_68}, journal = {Rough Sets and Knowledge Technology}, keywords = {multilevel}, pages = {468--475}, posted-at = {2007-11-30 22:49:15}, priority = {2}, title = {A Comparison of Three Graph Partitioning Based Methods for Consensus Clustering}, url = {http://dx.doi.org/10.1007/11795131\_68}, year = {2006} }

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TY - CHAP ID - citeulike:2033149 L3 - citeulike-article-id:2033149 TI - A Comparison of Three Graph Partitioning Based Methods for Consensus Clustering JF - Rough Sets and Knowledge Technology SP - 468 EP - 475 N2 - Consensus clustering refers to combining multiple clusterings over a common dataset into a consolidated better one. This paper compares three graph partitioning based methods. They differ in how to summarize the clustering ensemble in a graph. They are evaluated in a series of experiments, where component clusterings are generated by tuning parameters controlling their quality and resolution. Finally the combination accuracy is analyzed as a function of the learning dynamics vs. the number of clusterings involved. Keywords: Consensus clustering, graph partitioning, clustering ensemble, consensus function, data mining. KW - multilevel AU - Hu, Tianming AU - Zhao, Weiquan AU - Wang, Xiaoqiang AU - Li, Zhixiong PY - 2006/// UR - http://dx.doi.org/10.1007/11795131_68 ER -

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