Intuitionistic fuzzy $$c$$ c -means clustering algorithm with neighborhood attraction in segmenting medical image

Color Image Retrieval Based on Non-Parametric Statistical Tests of Hypothesis

“…Block-wise sampling technique proposed in [21] does not yield good results for the rotated and scaled images, because the corresponding blocks of query (actual) and target (transformed) images do not match spatially, while the target image is rotated or scaled. Hence, the technique proposed in [21] fails to match and retrieve the right images.…”

“…Hence, the technique proposed in [21] fails to match and retrieve the right images. The proposed system avoids this problem, because it uses the global distributional differences of both query and target images; in the case of structured images, these features are extracted from the shapes in both query and target images, and those are compared shape-wise, it compares the number of shapes between the images.…”

Weight computation of criteria in a decision-making problem by knowledge measure with intuitionistic fuzzy set and interval-valued intuitionistic fuzzy set

“…IFS, introduced by Atanassov (1986), is characterized by a membership function and a non-membership function, and, thus, can describe uncertain information in a more meaningful way than Zadeh's (1965) fuzzy set, which is characterized by only membership function. Owing to this fact, IFS theory has been investigated by many authors and have already been used for decision making (Khaleie and Fasanghari 2012;Li et al 2014;Tan 2011;Das et al 2014), medical diagnosis (De et al 2001;Huang et al 2015;Kharal 2009) and pattern recognition (Papakostas et al 2013;Farhadinia 2014), to name a few. The authors have also devoted considerable attention to the generalization of the IFS theory.…”

An Evolutionary Intuitionistic Fuzzy K-means Clustering Approach based Cluster Head Selection in MANET

“…Fuzzy set generates only membership function (x), x  X, whereas Intuitionistic fuzzy set (IFS) given by Atanassov [17] considers both membership (x) and non-membership (x). An Intuitionistic fuzzy set A in X, is written as the equation (12) Where are the membership and non-membership degrees of an element in the set A with the condition as mentioned in the equation (13) When  for every x in the set A, then the set A becomes a fuzzy set.…”

“…An Intuitionistic fuzzy set A in X, is written as the equation (12) Where are the membership and non-membership degrees of an element in the set A with the condition as mentioned in the equation (13) When  for every x in the set A, then the set A becomes a fuzzy set. For all Intuitionistic fuzzy sets, Atanassov also indicated a hesitation degree, which arises due to lack of knowledge in defining the membership degree of each element, x in the set A and is given by the equation (14) Due to hesitation degree, the membership values lie in the interval as mentioned in the equation (15) IFKM minimizes the objective function as in equation (16) Where, where denotes the Intuitionistic fuzzy membership and denotes the conventional fuzzy membership of the ith feature in rth cluster defined as follows in the equation (17) This paper adopted the distance measure proposed by D. M. Tsai and C. C. Lin. they incorporated a new distance measure into the conventional FCM as equation (19) and  is the weighted mean distance of cluster r and is given by equation (20) is hesitation degree, which is defined as follows in the equation (21) and is calculated from Yager's Intuitionistic fuzzy complement, the Intuitionistic fuzzy set becomes (22) Step1: Initializes the k Intuitionistic fuzzy cluster centers randomly…”