A rough-set based measurement for the membership degree of fuzzy C-means algorithm
Zhenhao Wang, Jiancong Fan · 2018
The traditional Fuzzy C-means (FCM) algorithm is stable and easy to be implemented. However, the data elements in the cluster boundary of FCM are easily clustered into incorrect classes making the efficiency of FCM algorithm reduced. Aiming at solving this problem, this paper presents a Rough-FCM algorithm which is combined FCM algorithm with rough set according to new equations. We take the advantage of the positive region set and the boundary region set of rough set. First, Rough-FCM algorithm divides the data elements into the positive region set or the boundary region set of all classes according to the threshold we set. Second, it updates the cluster centers and membership matrixes with new equations. Thus, we can execute the second clustering based on first clustering of FCM. By comparing the experimental results of the Rough-FCM with K-means, DBSCAN and FCM according to four clustering evaluation indexes on both synthetic and real datasets, we evaluate our proposed algorithm and improve outcomes from most of datasets by adopting these three classic clustering algorithms mentioned above.