Fuzzy model generation using Subtractive and Fuzzy C-Means clustering

摘要

Clustering is a process of partitioning similar data into groups. For this, number of clustering algorithms have been proposed in literature. Some of them can also be used for the generation of Fuzzy Models. In this work, Sugeno fuzzy models being generated by Subtractive and FCM clustering have been discussed. Experiments have been performed on real datasets to compare the Subtractive and FCM clustering. Further, the effect of increase in the radius size is analyzed in Subtractive clustering. The average absolute error and root mean square error is also found out when using FCM clustering and Subtractive clustering with different values of radius.