The Recovery Performance of Two-mode Clustering Methods: Monte Carlo Experiment
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In this paper, a Monte Carlo study on the performance of two-mode cluster methods is presented. The synthetical data sets were generated to correspond to two types of data consisting of overlapping as well as disjoint clusters. Furthermore, the data sets differed in cluster number, degrees of within-group homogeneity and between-group heterogeneity as well as degree of cluster overlap. We found that the methods performed very differently depending on type of data, number of clusters, homogeneity and cluster overlap.
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