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In this paper, the authors analyze different techniques for cluster validation to detect the number of clusters in a dataset and introduce a new combination approach to cluster validation. The proposed algorithm is a combination of several validation indexes, which are used to simultaneously evaluate different partitions of a dataset generated with different clustering techniques and object distances. The term "Cluster analysis" refers to the framework of algorithms used to capture the underlying group structure of unlabelled data sets.
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