1998
DOI: 10.1016/s0167-8655(97)00168-2
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A new cluster validity index for the fuzzy c-mean

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Cited by 333 publications
(40 citation statements)
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“…In this case, the homogeneous regions were first differentiated by using the fuzzy c-means clusters in terms of the coefficient variation of monthly precipitation within a year. Generally, five homogeneous regions were identified based on the cluster validity index for the fuzzy c-mean proposed by Ramze Rezaee et al (1998), and the results are not shown in the paper. The multivariate and univariate discordance test was done for each homogeneous region.…”
Section: Regionalization Of Droughts In the Pearl River Basinmentioning
confidence: 96%
“…In this case, the homogeneous regions were first differentiated by using the fuzzy c-means clusters in terms of the coefficient variation of monthly precipitation within a year. Generally, five homogeneous regions were identified based on the cluster validity index for the fuzzy c-mean proposed by Ramze Rezaee et al (1998), and the results are not shown in the paper. The multivariate and univariate discordance test was done for each homogeneous region.…”
Section: Regionalization Of Droughts In the Pearl River Basinmentioning
confidence: 96%
“…4d, e) provide further information on spatial characteristics of a selected clustering, i.e., about the spread of the members in one cluster. A central quantity in our workflow is the reduction of STDEV per model grid-point [60], providing direct information about the average compactness of a cluster (Fig. 4d, e).…”
Section: Visualizing Clustering "Value"mentioning
confidence: 99%
“…The latter fact motivated the research in the field of clustering validation notably more than the field of classification evaluation. It has been even stated that clustering validation is regarded as important as the clustering itself [32].…”
Section: Introductionmentioning
confidence: 99%