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.
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© 2006 Springer-Verlag Berlin Heidelberg
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Hu, T., Zhao, W., Wang, X., Li, Z. (2006). A Comparison of Three Graph Partitioning Based Methods for Consensus Clustering. In: Wang, GY., Peters, J.F., Skowron, A., Yao, Y. (eds) Rough Sets and Knowledge Technology. RSKT 2006. Lecture Notes in Computer Science(), vol 4062. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11795131_68
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DOI: https://doi.org/10.1007/11795131_68
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-36297-5
Online ISBN: 978-3-540-36299-9
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