 Fuzzy C-Means (FCM) is an unsupervised clustering method that has been used extensively in data analysis and image segmentation. The defuzzification of the fuzzy partition of FCM is usually done using the maximum membership degree principle which may not be appropriate for some real-world applications. In this paper, we present a new algorithm that generates a probabilistic model of the fuzzy...

This paper aims to introduce a scientific Semi-Supervised Fuzzy C-Mean (SSFCM) clustering approach for passenger cars classification based on the feature learning technique. The proposed method is able classify vehicles in micro, small, middle, upper large and luxury classes. performance of algorithm analyzed compared with an unsupervised fuzzy C-means (FCM) Swiss expert dataset. Experiment res...

Some of the well-known fuzzy clustering algorithms are based on Euclidean distance function, which can only be used to detect spherical structural clusters. Gustafson-Kessel clustering algorithm and Gath-Geva clustering algorithm were developed to detect non-spherical structural clusters. However, the former needs added constraint of fuzzy covariance matrix, the later can only be used for the d...

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...

One of the most famous algorithms that appeared in the area of image segmentation is the Fuzzy C-Means (FCM) algorithm. This algorithm has been used in many applications such as data analysis, pattern recognition, and image segmentation. It has the advantages of producing high quality segmentation compared to the other available algorithms. Many modifications have been made to the algorithm to ...

We introduce a new category of fuzzy neural networks with multiple-output based on fuzzy clustering algorithm, especially, fuzzy c-means clustering algorithm (FCM-based FNNm) for pattern classification in this paper. The premise part of the rules of the proposed networks is realized with the aid of the scatter partition of input space generated by FCM clustering algorithm. The partitioned local...

In view of failure characteristics of wind turbine gear box, this paper puts forward a method for fault diagnosis based on the ensemble local means decomposition (ELMD) and fuzzy C-means clustering (FCM) method. Resolve the vibration signal of different fault state of high speed gear box by ELMD to obtain the PF component, and obtain its singular value, which is composed of known sample and tes...

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...

This work describes a knowledge-guided clustering approach for mineral potential mapping (MPM), by which the optimum number of clusters is derived form a knowledge-driven methodology through a concentration-area (C-A) multifractal analysis. To implement the proposed approach, a case study at the North Narbaghi region in the Saveh, Markazi province of Iran, was investigated to discover porphyry ...

Pixel classification among overlapping land cover regions in remote sensing imagery is a challenging task. Detection of uncertainty and vagueness are always key features for classifying mixed pixels. This chapter proposes an approach for pixel classification using hybrid approach of Fuzzy C-Means and Particle Swarm Optimization methods. This new unsupervised algorithm is able to identify cluste...