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• W62664711 abstract "The goal of data clustering can be formally stated as a min-max clustering principle: data points are grouped into clusters such that (a) between-cluster similarities are minimized while (b) within-cluster similarities are maximized. Existing methods typically focus on one of the above requirements. Here we propose a new method that emphasizes both requirements simultaneously. The MinMaxCut method uses pairwise similarities between all data objects. Viewing them as the adjacency matrix of a weighted graph, MinMaxCut is a graph partitioning method. An important feature is that the continuous solution of the cluster membership indicator vector is the eigenvector of the generalized graph Laplacian matrix; this simplifies the optimization of the min-max clustering objective function. We provide a comprehensive analysis of this principled clustering method; a systematic evaluation on clustering experiments on Internet newsgroups indicates that MinMaxCut outperforms other current popular partitioning/clustering methods." @default.
• W62664711 created "2016-06-24" @default.
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• W62664711 date "2003-01-01" @default.
• W62664711 modified "2023-09-27" @default.
• W62664711 title "A MinMaxCut Spectral Method for Data Clustering and Graph Partitioning" @default.
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