Cluster ensembles for high dimensional clustering : an empirical study Public Deposited

http://ir.library.oregonstate.edu/concern/technical_reports/2z10wr52w

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  • This paper studies cluster ensembles for high dimensional data clustering. We examine three different approaches to constructing cluster ensembles. To address high dimensionality, we focus on ensemble construction methods that build on two popular dimension reduction techniques, random projection and principal component analysis (PCA). We present evidence showing that ensembles generated by random projection perform better than those by PCA and further that this can be attributed to the capability of random projection to produce diverse base clusterings. We also examine four different consensus functions for combining the clusterings of the ensemble. We compare their performance using two types of ensembles, each with different properties. In both cases, we show that a recent consensus function based on bipartite graph partitioning achieves the best performance.
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  • description.provenance : Submitted by Laura Wilson (laura.wilson@oregonstate.edu) on 2012-12-11T23:32:37Z No. of bitstreams: 1 2006-23.pdf: 241490 bytes, checksum: ef6cad37413068ef80ba9a5b340aca3b (MD5)
  • description.provenance : Made available in DSpace on 2012-12-11T23:33:59Z (GMT). No. of bitstreams: 1 2006-23.pdf: 241490 bytes, checksum: ef6cad37413068ef80ba9a5b340aca3b (MD5) Previous issue date: 2006
  • description.provenance : Approved for entry into archive by Laura Wilson(laura.wilson@oregonstate.edu) on 2012-12-11T23:33:59Z (GMT) No. of bitstreams: 1 2006-23.pdf: 241490 bytes, checksum: ef6cad37413068ef80ba9a5b340aca3b (MD5)

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