Fuzzy Clustering using Credibilistic Critical Values
نویسنده
چکیده
In this paper, the utility of credibilistic critical values in crisp conversion of fuzzy data sets is considered. Conversion of this type becomes essential mainly when clustering of fuzzy data sets is carried out. In this paper performance of two popular clustering algorithms namely Fuzzy c–means and Fuzzy c–medoids algorithms are evaluated under credibilistic critical value crisp conversion is carried out. Two synthetic data sets of varying nature are used in the comparative study. Some popular fuzzy clustering validity measures were employed in this study. KeywordsClustering, Critical values, Credibility space, Partition Coefficient, Partition Entropy, FS Index, XB Index
منابع مشابه
Particle Swarm Optimization Based Spatial Credibilistic Clustering Algorithm Applied in High Noise Image Segmentation
In practice, noise images even high noise images are very common. It’s very essential and critical to deal with such kind of images to process real-image segmentation and pattern recognition. In this paper, differences of credibilistic clustering algorithm (CCA) and fuzzy c-means algorithm (FCM) in dealing with noise images are studied and the research shows that in most case, CCA performs bett...
متن کاملCredibilistic Clustering: The Model and Algorithms
Fuzzy clustering is a widely used approach for data classification by using the fuzzy set theory. The probability measure and the possibility measure are two popular measures which have been used in the fuzzy c-means algorithm (FCM) and the possibilistic clustering algorithms (PCAs), respectively. However, the numerical experiments revealed that FCM and its derivatives lack the intuitive concep...
متن کاملHybrid Methods of Spatial Credibilistic Clustering and Particle Swarm Optimization in High Noise Image Segmentation
information from the original image as compared with crisp or hard segmentation methods. In practice, noisy images (even high noise images) are very common. It's very essential and critical to deal with such images to process real-image segmentation and pattern recognition. In this paper, differences of credibilistic clustering algorithm (CCA) and fuzzy c-means algorithm (FCM) in dealing with n...
متن کاملBilateral Weighted Fuzzy C-Means Clustering
Nowadays, the Fuzzy C-Means method has become one of the most popular clustering methods based on minimization of a criterion function. However, the performance of this clustering algorithm may be significantly degraded in the presence of noise. This paper presents a robust clustering algorithm called Bilateral Weighted Fuzzy CMeans (BWFCM). We used a new objective function that uses some k...
متن کاملDominances on fuzzy variables based on credibility measure
This paper studies three notions of fuzzy dominance based on credibility measure, namely, the fuzzy mean-risk dominance, the first credibilistic dominance and the second credibilistic dominance. More precisely, we introduce and examine some properties of the Fuzzy Lower Partial Moments (FLPM) of a fuzzy variable and, we deduce the Fuzzy Kappa index (FK) of a fuzzy variable, that is, a riskadjus...
متن کامل