Speeding up fuzzy c-means: using a organisation to control the precision hierarchical data of membership calculation
We examine the run-time behaviour of conventional fuzzy c-means implementations. Investigating into FCM termination conditions and membership update equations, we derive an approximative FCM that yields the same results as a conventional implementation within a given precision. We incorporate additional information about the data set by re-organizing the set as a tree. Our modification leads to an FCM algorithm with a significantly different run time behaviour, the gain of using the modified implementation increases with an increasing number of data objects and especially an increasing number of clusters, but is also sensitive to the chosen fuzzifier. (C) 2002 Elsevier Science B.V. All rights reserved.
fuzzy c-means run-time fuzzy clustering data hierarchy data tree approximative memberships