To deal with the problem of premature convergence of the fuzzy c-means clustering algorithm based on particle swarm optimization, which is sensitive to noise and less effective when handling the data set that dimensions greater than the number of samples, a novel fuzzy c-means clustering method based on the enhanced Particle Swarm Optimization algorithm is presented. Firstly, this approach dist...

To organize the wide variety of data sets automatically and acquire accurate classification, this paper presents a modified fuzzy c-means algorithm (SP-FCM) based on particle swarm optimization (PSO) and shadowed sets to perform feature clustering. SP-FCM introduces the global search property of PSO to deal with the problem of premature convergence of conventional fuzzy clustering, utilizes vag...

A fuzzy clustering based modification of Gaussian mixture models (GMMs) for speaker recognition is proposed. In this modification, fuzzy mixture weights are introduced by redefining the distances used in the fuzzy c-means (FCM) functionals. Their reestimation formulas are proved by minimising the FCM functionals. The experimental results show that the fuzzy GMMs can be used in speaker recogniti...

In the area of clustering, there are numerous approaches to construct clusters in the input space. For regression problem, when forming clusters being a part of the overall model, the relationships between the input space and the output space are essential and have to be taken into consideration. Conditional Fuzzy C-Means (c-FCM) clustering offers an opportunity to analyze the structure in the ...

Protein sequence motifs are very important to the analysis of biologically significant conserved regions to determine the conformation, function and activities of the proteins. These sequence motifs are identified from protein sequence segments generated from large number of protein sequences. All generated sequence segments may not yield potential motif patterns. In this paper, short recurring...

In spite of its computational efficiency and wide spread popularity, the FCM algorithm does not take the spatial information of pixels into consideration. In this paper, a multiple kernel fuzzy c-means clustering (MKFCM) algorithm is presented for fuzzy segmentation of magnetic resonance (MR) images. By introducing a novel adaptive method to compute the weights of local spatial values in the ob...

This paper introduces two clustering methods to be used in the analysis of network traffic. The methods are the self-organizing map (SOM) and the fuzzy c-means clustering (FCM) algorithm. First the basic theory of both methods is presented. Before the clustering process, a comprehensive data preprocessing is performed. Methods are used to produce application profiles and clusters from network t...

Image segmentation plays an important role for machine vision applications. In this paper, we present a new segmentation strategy based on fuzzy clustering algorithm. The new algorithm includes the spatial interactions by assuming that the statistical model of segmented image regions is Gibbs Random Field ( GRF ). We specitjl the neighborhood system, the associated cliques. and the potentials o...

We show that the results obtained through clusteringbased image segmentation of single or multi-component images can be improved by a fuzzy relaxation of the degrees of membership in the image space. We illustrate the point through two clustering techniques: the fuzzy Cmeans (FCM) technique and a clustering technique based on the estimation of the probability density function (pdf).

this paper presents a fuzzy decision-making approach to deal with a clustering supplier problem in a supply chain system. during recent years, determining suitable suppliers in the supply chain has become a key strategic consideration. however, the nature of these decisions is usually complex and unstructured. in general, many quantitative and qualitative factors, such as quality, price, and fl...