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Scalable Clustering by Truncated Fuzzy c-means

1. Department of Mathematics University of Connecticut 341 Mansfield Road, Storrs, CT 06269-1009, USA;
2. Business School Hunan University Changsha, Hunan 410082, China;
3. Columbian College of Arts & Sciences George Washington University Washington, D. C., 20052, USA

Most existing clustering algorithms are slow for dividing a large dataset into a large number of clusters. In this paper, we propose a truncated FCM algorithm to address this problem. The main idea behind our proposed algorithm is to keep only a small number of cluster centers during the iterative process of the FCM algorithm. Our numerical experiments on both synthetic and real datasets show that the proposed algorithm is much faster than the original FCM algorithm and the accuracy is comparable to that of the original FCM algorithm.
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Keywords Data clustering; fuzzy c-means; scalable clustering

Citation: Guojun Gan, Qiujun Lan, Shiyang Sima. Scalable Clustering by Truncated Fuzzy c-means. Big Data and Information Analytics, 2016, 1(2): 247-259. doi: 10.3934/bdia.2016007

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This article has been cited by

  • 1. Guojun Gan, Valuation of Large Variable Annuity Portfolios Using Linear Models with Interactions, Risks, 2018, 6, 3, 71, 10.3390/risks6030071
  • 2. Guojun Gan, Emiliano A. Valdez, Data Clustering with Actuarial Applications, North American Actuarial Journal, 2019, 1, 10.1080/10920277.2019.1575242

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Copyright Info: 2016, Guojun Gan, et al., licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution Licese (http://creativecommons.org/licenses/by/4.0)

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