Speeding up fuzzy c-means: using a hierarchical data organisation to control the precision of membership calculation

Authors:
Frank Höppner
Affiliations:
Department of Electrical Engineering and Computer Science, University of Applied Sciences, Constantiaplatz 4, D-26723 Emden, Germany
Venue:
Fuzzy Sets and Systems - Clustering and modeling
Year:
2002

Citing 4
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Abstract

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 reorganizing 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.