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Fast, fuzzy c‐means clustering of data sets with many features
Author(s) -
Alsberg Bjørn K.
Publication year - 1995
Publication title -
journal of computational chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.907
H-Index - 188
eISSN - 1096-987X
pISSN - 0192-8651
DOI - 10.1002/jcc.540160404
Subject(s) - cluster analysis , feature (linguistics) , fuzzy clustering , computer science , flame clustering , data mining , determining the number of clusters in a data set , covariance , correlation clustering , cure data clustering algorithm , pattern recognition (psychology) , cluster (spacecraft) , point (geometry) , feature vector , artificial intelligence , algorithm , mathematics , statistics , philosophy , linguistics , geometry , programming language
A fuzzy c‐means clustering algorithm is presented which is much faster than the traditional algorithm for data sets in which the number of features is significantly larger than the number of feature vectors. The algorithm is constructed by utilizing the covariance structure of feature vectors and cluster centers. By using results from a previous clustering, modified versions of the new algorithm achieve additional reductions in floating point operations. © 1995 by John Wiley & Sons, Inc.