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• W100104462 abstract "Previous chapter Next chapter Full AccessProceedings Proceedings of the 2004 SIAM International Conference on Data Mining (SDM)A Mixture Model for Clustering EnsemblesAlexander Topchy, Anil K. Jain, and William PunchAlexander Topchy, Anil K. Jain, and William Punchpp.379 - 390Chapter DOI:https://doi.org/10.1137/1.9781611972740.35PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract Clustering ensembles have emerged as a powerful method for improving both the robustness and the stability of unsupervised classification solutions. However, finding a consensus clustering from multiple partitions is a difficult problem that can be approached from graph-based, combinatorial or statistical perspectives. We offer a probabilistic model of consensus using a finite mixture of multinomial distributions in a space of clusterings. A combined partition is found as a solution to the corresponding maximum likelihood problem using the EM algorithm. The excellent scalability of this algorithm and comprehensible underlying model are particularly important for clustering of large datasets. This study compares the performance of the EM consensus algorithm with other fusion approaches for clustering ensembles. We also analyze clustering ensembles with incomplete information and the effect of missing cluster labels on the quality of overall consensus. Experimental results demonstrate the effectiveness of the proposed method on large real-world datasets. Previous chapter Next chapter RelatedDetails Published:2004ISBN:978-0-89871-568-2eISBN:978-1-61197-274-0 https://doi.org/10.1137/1.9781611972740Book Series Name:ProceedingsBook Code:PR117Book Pages:xiv + 537Key words:unsupervised learning, clustering ensemble, consensus function, mixture model, EM algorithm" @default.
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• W100104462 title "A Mixture Model for Clustering Ensembles" @default.
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• W100104462 doi "https://doi.org/10.1137/1.9781611972740.35" @default.
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