Showing papers on "Rand index published in 1997"
TL;DR: In this paper, four commonly used clustering methods (UPGMA, Ward Linkage, Complete Linkage and TWINSPAN) were compared in their ability to understand the structure of three river macroinvertebrates datasets.
Abstract: Four commonly used clustering methods (UPGMA, Ward Linkage,Complete Linkage and TWINSPAN) were compared in their abilitytorecognise the structure of three river macroinvertebratesdatasetswhich were pre-determined based on habitat and biologicalcharacteristics or chemical water quality of sampling sites.DCA,NMDS and ANOSIM were applied to the same datasets to providefurther information about data structure, and nonparametrictestswere also undertaken on major chemical variables to justifythepredeterminations. The modified Rand Index was used to measuretheagreement between a particular solution and the pre-determinedclassification. The results showed that Ward Linkage performedbestwhen its use was broadened and used with the CY DissimilarityMeasure, followed by TWINSPAN and Complete Linkage with UPGMAbeingleast successful. There was evidence to suggest that theeffectiveness of some clustering methods (e.g. UPGMA) may varyatdifferent clustering levels, and simulation techniques whichhavebeen used to assess clustering methods could leave somepropertiesof clustering methods unexamined.
63 citations
TL;DR: This paper follows the approach developed by Hubert and Arabie (1985) and exploits the similarity between partition comparison and the analysis of multi-way contingency tables and considers several hypotheses of independence among clusterings, including less "extreme" null models (e.g. conditional independence).
Abstract: A number of indices have been suggested in the classification and in the psychometric literature for the purpose of comparing partitions. However, interest has mainly focused on the two-dimensional case. In this paper we tackle the problem of comparing three partitions. We follow the approach developed by Hubert and Arabie (1985) and exploit the similarity between partition comparison and the analysis of multi-way contingency tables. We adopt an inferential point of view and consider several hypotheses of independence among clusterings, including less "extreme" null models (e.g. conditional independence) which can be useful in practice. As is customary in this setting, we condition on both the number of clusters and the number of objects in each cluster, and we derive some exact results for our statistics. An illustrative application of the method is also presented.
1 citations