Fuzzy Cluster Analysis

By K. Manton, G. Lowrimore, A. Yashin, M. Kovtun. 2005

411 citations recorded by the source corpus.

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Risk-Constrained Multi-Stage Wind Power Investment (2013, methodology): “Thus, we use the K-means clustering mechanism [13], [14] to reduce the original data set into a tractable set maintaining the information and correlation of load consumption and wind power generation.”
Is there a need for fuzzy logic? (2008, background): “control ? fuzzy control [12,20] linear programming ? fuzzy linear programming [108,21] probability theory ? fuzzy probability theory [102,104,107] measure theory ? fuzzy measure theory [14,66] topology ? fuzzy topology [73,41] graph theory ? fuzzy graph theory [36,44] cluster analysis ? fuzzy cluste”
Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering (2007, methodology): “Grouping can be realized by various techniques; e.g., K-means clustering [14], self-organizing maps [15], fuzzy clustering [16], vector quantization [17], and others.”
A survey on nature inspired metaheuristic algorithms for partitional clustering (2014, methodology): “The popular algorithms include fuzzy c-means (FCM) [14] and fuzzy c-shells algorithm (FCS) [15].”
Multiple Kernel Fuzzy Clustering (2012, background): “Thus, for items that belong to two or more clusters, it may be more appropriate to assign them with gradual memberships to avoid coarse-grained assignments of data [6].”