publication . Article . 2003

Fast accurate fuzzy clustering through data reduction

S. Eschrich; null Jingwei Ke; L.O. Hall; D.B. Goldgof;
Open Access
  • Published: 08 Apr 2003 Journal: IEEE Transactions on Fuzzy Systems, volume 11, pages 262-270 (issn: 1063-6706, Copyright policy)
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Abstract
Clustering is a useful approach in image segmentation, data mining, and other pattern recognition problems for which unlabeled data exist. Fuzzy clustering using fuzzy c-means or variants of it can provide a data partition that is both better and more meaningful than hard clustering approaches. The clustering process can be quite slow when there are many objects or patterns to be clustered. This paper discusses the algorithm brFCM, which is able to reduce the number of distinct patterns which must be clustered without adversely affecting the partition quality. The reduction is done by aggregating similar examples and then using a weighted exemplar in the cluster...
Subjects
ACM Computing Classification System: ComputingMethodologies_PATTERNRECOGNITION
free text keywords: Artificial intelligence, business.industry, business, Single-linkage clustering, Constrained clustering, Pattern recognition, Correlation clustering, Data mining, computer.software_genre, computer, Machine learning, Canopy clustering algorithm, Cluster analysis, Fuzzy clustering, Data stream clustering, Mathematics, CURE data clustering algorithm
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