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CiteWeb id: 20120000057

CiteWeb score: 3602

Many intuitively appealing methods have been suggested for clustering data, however, interpretation of their results has been hindered by the lack of objective criteria. This article proposes several criteria which isolate specific aspects of the performance of a method, such as its retrieval of inherent structure, its sensitivity to resampling and the stability of its results in the light of new data. These criteria depend on a measure of similarity between two different clusterings of the same set of data; the measure essentially considers how each pair of data points is assigned in each clustering.

The publication "Objective Criteria for the Evaluation of Clustering Methods" is placed in the Top 10000 of the best publications in CiteWeb. Also in the category Mathematics it is included to the Top 1000. Additionally, the publicaiton "Objective Criteria for the Evaluation of Clustering Methods" is placed in the Top 100 among other scientific works published in 2012.
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