Applying the Possibilistic c-Means Algorithm in Kernel-Induced Spaces

Kernel-based fuzzy and possibilistic c-means clustering

The 'kernel method' has attracted great attention with the development of support vector machine (SVM) and has been studied in a general way. In this paper, this 'method' is extended to the well-known fuzzy c-means (FCM) and possibilistic c-means (PCM) algorithms. It is realized by substitution of a kernel-induced distance metric for the original Euclidean distance, and the corresponding algori...

Hierarchical Intuitionistic Fuzzy Possibilistic C Means Kernel Clustering Algorithm for Distributed Networks

Advances in distributed networking have resulted in an explosion in size of modern datasets while storage and processing power continue to lag behind. This requires the need for algorithms that are efficient in terms of number of measurements and running time. To combat challenges associated with large datasets in distributed networks we propose hierarchical intuitionistic fuzzy possibilistic c...

Wbc Image Segmentation Using Modified Fuzzy Possibilistic C - Means Algorithm

Medical Image Segmentation becomes vital process for its proper detection and diagnosis of diseases. In which accurate White Blood Cells segmentation becomes important issue because differential counting, plays a major role in the determination the diseases and based on it the treatment is followed for the patients. To address this work here various fuzzy based clustering techniques are propose...

Possibilistic C-Means in Scene Matching

Determining if two images acquired at different times and under different viewing conditions contain the same scene is a difficult problem in computer vision. We demonstrate an approach that utilizes spatial relationships among the objects in the two scenes that ultimately produces a mapping of objects from one view to the other, and as a bonus, recovers the viewing transformation parameters. T...

Fuzzy C-Means Algorithm with Divergence-Based Kernel