A novel intuitionistic fuzzy C means clustering algorithm and its application to medical images

详细信息    查看全文

• 作者：Tamalika Chaira ; ^a ; ^{tchaira@yahoo.com}
• 关键词：Intuitionistic fuzzy set ; Hesitation degree ; Fuzzy clustering ; Intuitionistic fuzzy entropy ; Intuitionistic fuzzy generator
• 刊名：Applied Soft Computing Journal
• 出版年：2011
• 出版时间：March 2011
• 年：2011
• 卷：11
• 期：2
• 页码：1711-1717
• 全文大小：588 K

文摘

This paper presents a novel intuitionistic fuzzy C means clustering method using intuitionistic fuzzy set theory. The intuitionistic fuzzy set theory considers another uncertainty parameter which is the hesitation degree that arises while defining the membership function and thus the cluster centers may converge to a desirable location than the cluster centers obtained using fuzzy C means algorithm. Also a new objective function which is the intuitionistic fuzzy entropy is incorporated in the conventional fuzzy C means clustering algorithm. This is done to maximize the good points in the class. This clustering method is used in clustering different regions of the CT scan brain images and these may be used to identify the abnormalities in the brain. Experimental results show the effectiveness of the proposed method in contrast to conventional fuzzy C means algorithms and also type II fuzzy algorithm.