Due to its inferior characteristics, an observed (noisy) image's direct use gives rise poor segmentation results. Intuitively, using noise-free image can favorably impact segmentation. Hence, the accurate estimation of residual between and images is important task. To do so, we elaborate on residual-driven Fuzzy C-Means (FCM) for segmentation, which first approach that realizes leads participat...

In this paper we discuss the solution of the clustering problem usually solved by the K-means algorithm. The problem is known to have local minimum solutions.A simulated annealing algorithm for the clustering problem. The solution of the clustering problem usually solved by the K-means algorithm.In this paper, we explore the applicability of simulated annealing. Clustering problem is investigat...

This article describes a multiobjective spatial fuzzy clustering algorithm for image segmentation. To obtain satisfactory segmentation performance for noisy images, the proposed method introduces the non-local spatial information derived from the image into fitness functions which respectively consider the global fuzzy compactness and fuzzy separation among the clusters. After producing the set...

Soil Classification deals with the systematic categorization of soils based on distinguished characteristics as well as criteria. We developed Data Mining techniques like: GATree, Fuzzy Classification rules and Fuzzy C Means algorithm for classifying soil texture in agriculture soil data. In this paper, we give a comparative study of developed algorithms. The study is used to compare and analyz...

In this study, we present a comprehensive comparative analysis of kernel-based fuzzy clustering and fuzzy clustering. Kernel based clustering has emerged as an interesting and quite visible alternative in fuzzy clustering, however, the effectiveness of this extension vis-à-vis some generic methods of fuzzy clustering has neither been discussed in a complete manner nor the performance of cluster...

Recently Kernelized Fuzzy C-Means clustering technique where a kernel-induced distance function is used as a similarity measure instead of a Euclidean distance which is used in the conventional Fuzzy C-Means clustering technique, has earned popularity among research community. Like the conventional Fuzzy C-Means clustering technique this technique also suffers from inconsistency in its performa...

ABSTACT This paper presents a new method for solving Substation Expansion Planning (SEP) problem using three basic algorithms in fuzzy clustering. Clustering algorithms are mainly associated with distance functions and measure dissimilarities of data set in different clusters. It is equivalent to measure similarities of data in a cluster. That is, a lot of varieties exist to find and create suc...