A Review Of Monte Carlo Tests Of Cluster Analysis

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Abstract
A review of Monte Carlo validation studies of clustering algorithms is presented. Several validation studies have tended to support the view that Ward's minimum variance hierarchical method gives the best recovery of cluster structure. However, a more complete review of the validation literature on clustering indicates that other algorithms may provide better recovery under a variety of conditions. Applied researchers are cautioned concerning the uncritical selection of Ward's method for empirical research. Alternative explanations for the differential recovery performance are explored and recommendations are made for future Monte Carlo experiments.

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