Computer Science ›› 2017, Vol. 44 ›› Issue (9): 45-48, 66.doi: 10.11896/j.issn.1002-137X.2017.09.008

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Determining Clustering Number of FCM Algorithm Based on DTRS

SHI Wen-feng and SHANG Lin   

  • Online:2018-11-13 Published:2018-11-13

Abstract: Fuzzy C-Means(FCM),as the most popular algorithm of the soft clustering,has been extensively used to make compact and well separated clusters.However,its sensitivity to initial cluster number makes choosing a better C value become very important.So it is an important step to determine the number of FCM clustering when we use FCM to do cluster analysis.In this paper,the extended decision-theoretic rough sets(DTRS) model is applied for the purpose of clustering validity analysis which could overcome the defect of the FCM algorithm.We proposed the method for determining clustering number of FCM algorithm based on DTRS,and we verified the effect of the clustering by image segmentation.Good segmentation results can be obtained when we compare the cost of different number of clusters.We compared our results with the ant colony fuzzy c-means hybrid algorithm (AFHA),which was proposed by Z.Yu et al in 2015,and the improved AFHA (IAFHA).The experimental results show that our clustering result is better in Bezdek partition coefficient with a higher value than AFHA and IAFHA algorithms,and in the Xie-Beni index as well.

Key words: Fuzzy C-Means,Decision-theoretic rough sets,Image segmentation

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