Journal article
Fuzzy c-Means algorithms for very large data
TC Havens, JC Bezdek, C Leckie, LO Hall, M Palaniswami
IEEE Transactions on Fuzzy Systems | Published : 2012
Abstract
Very large (VL) data or big data are any data that you cannot load into your computers working memory. This is not an objective definition, but a definition that is easy to understand and one that is practical, because there is a dataset too big for any computer you might use; hence, this is VL data for you. Clustering is one of the primary tasks used in the pattern recognition and data mining communities to search VL databases (including VL images) in various applications, and so, clustering algorithms that scale well to VL data are important and useful. This paper compares the efficacy of three different implementations of techniques aimed to extend fuzzy c-means (FCM) clustering to VL dat..
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Awarded by National Science Foundation
Funding Acknowledgements
Manuscript received September 6, 2011; revised January 27, 2012; accepted April 18, 2012. Date of publication May 25, 2012; date of current version November 27, 2012. This work was supported in part by Grant #1U01CA143062-01, Radiomics of Non-Small Cell Lung Cancer from the National Institutes of Health, and in part by the Michigan State University High Performance Computing Center and the Institute for Cyber Enabled Research. The work of T. C. Havens was supported by the National Science Foundation under Grant #1019343 to the Computing Research Association for the CI Fellows Project.