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A decision theoretic rough fuzzy c-means algorithm
Published in IEEE
Pages: 192 - 196
Imprecision based data clustering algorithms have gained a lot of importance these days because of the imprecise character of modern day databases. Some such algorithms are the rough c-means (RCM), fuzzy c-means (FCM) and their hybrid versions. Li et al used the decision theoretic rough set (DTRS) model by the way improving the RCM. In their approach they have used a notion called loss function to limit the information lost due to neighbours. The method of allocation using decision-theoretic rough sets model deals with potentially high computational cost. It has been observed that hybrid models are better than individual models. Keeping this in view, here we develop a clustering algorithm using DTRS and fuzzy sets in view called the decision-theoretic rough fuzzy c-means (DTRFCM). Experiments carried out show that our approach is more efficient than the DTRS algorithm. For this purpose we used several well-known data sets and parameters like the DB-index, D-index and Accuracy measure. © 2015 IEEE.
About the journal
JournalData powered by Typeset2015 IEEE International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN)
PublisherData powered by TypesetIEEE
Open Access0