General fuzzy min-max neural network for clustering and classification

By Bogdan Gabrys, Andrzej Bargiela. IEEE Trans. Neural Networks Learn. Syst., 2000

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Granular prototyping in fuzzy clustering (2004, background): “Granular computing is an important methodological endeavor and dwells quite substantially on fuzzy clustering, especially its niche addressing aspects of granular prototypes and granular constructs, in general.”
Fuzzy min–max neural networks for categorical data: application to missing data imputation (2012, methodology): “The most important difference from Gabrys and Bargiela’s network is the input layer, where, apart from the 2n numerical variable nodes, there are r additional nodes for the input categories, each having two connections with the second-layer nodes—one for each category eji, fji defining the Bj hyperb”
Survey of clustering algorithms (2005, background): “Determine the next generation by keeping the individuals with the highest ﬁ tness.”