Machine learningClustering
Fuzzy C-Means Clustering (FCM)
Fuzzy C-Means is a soft clustering algorithm in which every data point belongs to every cluster with a graded membership between 0 and 1, rather than being assigned to exactly one cluster. Originated by Joseph Dunn in 1973 and generalized by James Bezdek in 1981, it minimizes a fuzzy-weighted within-cluster variance, making it well suited to data whose groups overlap or have no sharp boundaries.
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Sources
- Dunn, J. C. (1973). A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. Journal of Cybernetics, 3(3), 32–57. DOI: 10.1080/01969727308546046 ↗
- Bezdek, J. C. (1981). Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press. ISBN: 978-0-306-40671-3