Keywords: Cluster analysis Fuzzy clustering Fuzzy c-means (FCM) Initialization Bias correction Probability weight a b s t r a c t Fuzzy clustering is generally an extension of hard clustering and it is based on fuzzy membership partitions. In fuzzy clustering, the fuzzy c-means (FCM) algorithm is the most commonly used clustering method. Numerous studies have presented various generalizations o...

Due to the limitation of the local spatial information in an image, fuzzy c-means clustering algorithms with the local spatial information cannot obtain the satisfying segmentation performance on the image heavily contaminated by noise. In order to compensate this drawback of the local spatial information, an effective kind of non-local spatial information is extracted from the image in this pa...

The paper deals with the problem of the fuzzy data clustering. In other words, objects attributes can be represented by fuzzy numbers or fuzzy intervals. A direct algorithm of possibilistic clustering is the basis of an approach to the fuzzy data clustering. The paper provides the basic ideas of the method of clustering and a plan of the direct possibilistic clustering algorithm. Definitions of...

 In this paper, utilization of clustering algorithms for data fusion in decision level is proposed. The results of automatic isolated word recognition, which are derived from speech spectrograph and Linear Predictive Coding (LPC) analysis, are combined with each other by using fuzzy clustering algorithms, especially fuzzy k-means and fuzzy vector quantization. Experimental results show that the...

Particle swarm optimization (PSO) is recent approach that can be employed in a wide range of applications. It is an evolutionary computing method based on colony aptitude which is a better parallel searching algorithm. Image segmentation is a low level vision task which is applicable in various applications such as object recognition, medical imaging, document analysis, just to name a few. PSO ...

Data clustering is one of the most important areas of research in data mining and knowledge discovery. Recent research in this area has shown that the best clustering results can be achieved using multi-objective methods. In other words, assuming more than one criterion as objective functions for clustering data can measurably increase the quality of clustering. In this study, a model with two ...

This paper presents the results of a new approach for binarization of text images. The new technique uses the fuzzy C-means clustering algorithm to simulate the clustering performed by the human visual system. The clustering process was applied to eleven text images. All of them have black text but the background change in color. As a mean of comparison, binarization using the image histogram w...

Abstract—This paper is concerned with knowledge representation and extraction of fuzzy if-then rules using Interval Type-2 Context-based Fuzzy C-Means clustering (IT2-CFCM) with the aid of fuzzy granulation. This proposed clustering algorithm is based on information granulation in the form of IT2 based Fuzzy C-Means (IT2-FCM) clustering and estimates the cluster centers by preserving the homoge...

In data mining, clustering is one of the important issues for separation and classification with groups like unsupervised data. In this paper, an attempt has been made to improve and optimize the application of clustering heuristic methods such as Genetic, PSO algorithm, Artificial bee colony algorithm, Harmony Search algorithm and Differential Evolution on the unlabeled data of an Iranian bank...