Fuzzy C-Means (FCM) and hard clustering are the most common tools for data partitioning. However, the presence of noisy observations in the data may cause generation of completely unreliable partitions from these clustering algorithms. Also, application of the Euclidean distance in FCM only produces spherical clusters. In this paper, a new noise-rejection clustering algorithm based on Mahalanob...

Segmentation in ultrasound images is challenging due to the interference from speckle noise and fuzziness of boundaries. In this paper, a segmentation scheme using fuzzy c-means (FCM) clustering incorporating both intensity and texture information of images is proposed to extract breast lesions in ultrasound images. Firstly, the nonlinear structure tensor, which can facilitate to refine the edg...

In fuzzy clustering, the fuzzy c-means (FCM) clustering algorithm is the best known and used method. An interesting extension of FCM is the fuzzy ISODATA (FISODATA) algorithm; it updates cluster number during the algorithm. That's why we can have more or less clusters than the initialization step. It's the power of the fuzzy ISODATA algorithm comparing to FCM. The aim of this paper is...

One of the strategies a company uses to retain its customers is Customer Relationship Management (CRM). CRM manages interactions and supports business build mutually beneficial relationships between companies customers. The utilization information technology, such as data mining used manage data, critical in order be able find out patterns made by when processing transactions. Clustering techni...

Fuzzy C-means (FCM) is an unsupervised clustering technique that is often used for the unsuper-vised segmentation of multivariate images. In traditional FCM the clustering is based on spectral information only and the geometrical relationship between neighbouring pixels is not used in the clustering procedure. In this paper, the spatially guided FCM (SG-FCM) algorithm is presented which segment...

-Clustering algorithms are an integral part of both computational intelligence and pattern recognition. It is unsupervised methods for classifying data into subgroups with similarity based inter cluster and intra cluster. In fuzzy clustering algorithms, mainly used algorithm is Fuzzy c-means (FCM) algorithm. This FCM algorithm is efficient only for spherical data when the input of the data stru...

The Fuzzy c-means algorithm (FCM) is proved to converge to either local minimum or saddle point by Bezdek et al.. However, it is problematical to judge the local minimum of a solution of the FCM in an easy way. In this paper, the Hessian matrix of one reduced objective function of the FCM is got and analyzed. Based on this study, a new optimality test of fixed points of the FCM is given, and it...

This paper considers the problem of partitioning noisy images into different regions by fuzzy clustering approach. Based on two fuzzy c-means (FCM) algorithms (FCM S1 and FCM S2), we propose four adaptive algorithms (FCM S11, FCM S12, FCM S21 and FCM S22) which utilize the high correlation of image pixels to increase the algorithms’ robustness to noise. Unlike existing algorithms, our algorithm...