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Validity indices have been investigated for decades. However, since there is no study of noise-resistance performance of these indices in the literature, there is no guideline for determining the best clustering in noisy data sets, especially microarray data sets. In this paper, we propose a generalized parametric validity (GPV) index which employs two tunable parameters a and b to control the proportions of objects being considered to calculate the dissimilarities. The greatest advantage ofdoi:10.1109/tcbb.2014.2312006 pmid:26356344 fatcat:lka3tsisqvdubfz67ykc2zueje