Erendira Rendon

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Clustering in data mining is a discovery process that groups a set of data so as to maximize the intra-cluster similarity and to minimize the inter-cluster similarity. Clustering becomes more challenging when data are categorical and the amount of available memory is less than the size of the data set. In this paper, we introduce CBC (Clustering Based on(More)
The procedure of evaluating the results of a clustering algorithm is known under the term cluster validity. In general terms, cluster validity criteria can be classified in three categories: internal, external and relative. In this work we focus on the external and internal criteria. External indexes require a priori data for the purposes of evaluating the(More)
Clustering aims at extracting hidden structures in datasets. Many validity indices have been proposed to evaluate clustering results; some of them work well when clusters have different densities and sizes and others with different shapes. They usually have a tendency to consider one or two characteristics simultaneously. In this paper, we present a cluster(More)
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