Fuzzy C-means Method for Clustering Microarray Data

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Multiconstrained gene clustering based on generalized projections (2010, methodology): “Typical gene clustering methods include hierarchical clustering [1], the k-means algorithm [2], self-organizing maps [3], the fuzzy c-means algorithm [4], and hidden Markov models [5].”
FLAME, a novel fuzzy clustering method for the analysis of DNA microarray data (2007, methodology): “Fuzzy C-means (FCM), also named Fuzzy K-means, is a fuzzy extension of K-means clustering and bases its fuzzy assignment essentially on the relative distance between one object and all cluster centroids [13,14].”
Time-series clustering - A decade review (2015, background): “In fuzzy clustering, an object has a degree of membership in each cluster [177].”
Survey of clustering algorithms (2005, background): “Since many genes usually display more than one function, fuzzy clustering may be more effective in exposing these relations [73].”
Fuzzy c-Means Algorithms for Very Large Data (2012, methodology): “5There are many methods to determine the optimal fuzzifier m [46]–[48].”