General fuzzy min-max neural network for clustering and classification

This paper describes a general fuzzy min-max (GFMM) neural network which is a generalization and extension of the fuzzy min-max clustering and classification algorithms of Simpson (1992, 1993). The GFMM method combines supervised and unsupervised learning in a single training algorithm. The fusion of clustering and classification resulted in an algorithm that can be used as pure clustering, pure classification, or hybrid clustering classification. It exhibits a property of finding decision boundaries between classes while clustering patterns that cannot be said to belong to any of existing classes. Similarly to the original algorithms, the hyperbox fuzzy sets are used as a representation of clusters and classes. Learning is usually completed in a few passes and consists of placing and adjusting the hyperboxes in the pattern space; this is an expansion-contraction process. The classification results can be crisp or fuzzy. New data can be included without the need for retraining. While retaining all the interesting features of the original algorithms, a number of modifications to their definition have been made in order to accommodate fuzzy input patterns in the form of lower and upper bounds, combine the supervised and unsupervised learning, and improve the effectiveness of operations. A detailed account of the GFMM neural network, its comparison with the Simpson's fuzzy min-max neural networks, a set of examples, and an application to the leakage detection and identification in water distribution systems are given

Analisis Perbandingan Metode Fuzzy C-means Dan Subtractive Fuzzy C-means

Fuzzy C-Means (FCM) is one of the most frequently used clustering method. However FCM has some disadvantages such as number of clusters to be prespecified and partition matrix to be randomly initiated which makes clustering result becomes inconsistent. Subtractive Clustering (SC) is an alternative method that can be used when number of clusters are unknown. Moreover, SC produces consistent clustering result. A hybrid method of FCM and SC called Subtractive Fuzzy CMeans (SFCM) is proposed to overcome FCM's disadvantages using SC. Both SFCM and FCM are implemented to cluster generated data and the result of the two methods are compared. The experiment shows that generally SFCM produces better clustering result than FCM based on six validity indices

Analisis Perbandingan Metode Fuzzy C-means Dan Subtractive Fuzzy C-means

Fuzzy C-Means (FCM) is one of the most frequently used clustering method. However FCM has some disadvantages such as number of clusters to be prespecified and partition matrix to be randomly initiated which makes clustering result becomes inconsistent. Subtractive Clustering (SC) is an alternative method that can be used when number of clusters are unknown. Moreover, SC produces consistent clustering result. A hybrid method of FCM and SC called Subtractive Fuzzy CMeans (SFCM) is proposed to overcome FCM\u27s disadvantages using SC. Both SFCM and FCM are implemented to cluster generated data and the result of the two methods are compared. The experiment shows that generally SFCM produces better clustering result than FCM based on six validity indices

A fuzzy c-means bi-sonar-based Metaheuristic Optimization Algorithm

Fuzzy clustering is an important problem which is the subject of active research in several real world applications. Fuzzy c-means (FCM) algorithm is one of the most popular fuzzy clustering techniques because it is efficient, straightforward, and easy to implement. Fuzzy clustering methods allow the objects to belong to several clusters simultaneously, with different degrees of membership. Objects on the boundaries between several classes are not forced to fully belong to one of the classes, but rather are assigned membership degrees between 0 and 1 indicating their partial membership. However FCM is sensitive to initialization and is easily trapped in local optima. Bi-sonar optimization (BSO) is a stochastic global Metaheuristic optimization tool and is a relatively new algorithm. In this paper a hybrid fuzzy clustering method FCB based on FCM and BSO is proposed which makes use of the merits of both algorithms. Experimental results show that this proposed method is efficient and reveals encouraging results

Optimized Fuzzy Cmeans – Fuzzy Covariance – Fuzzy Maximum Likelihood Estimation Clustering Method Based on Deferential Evolutionary Optimization Algorithm for Identification of Rock Mass Discontinuities Sets

Detecting of joint sets (clusters) is one of the most important processes in determining properties of fractures. Joints clustering and consequently, determination of the mean value representing each cluster is applicable to most rock mass studies. It is clear that the accuracy of the clustering process plays a key role in analyzing stability of infrastructures such as dams and tunnels and so on. Hence, in this paper, by reviewing several methods proposed for clustering fractures and considering their advantages and disadvantages, a three-stage hybrid method is developed which contains Fuzzy c-means, Fuzzy covariance and Fuzzy maximum likelihood estimation that by utilizing the modified orientation matrix had been optimized. This method is optimized by the Differential Evolutionary algorithm using a new and strong cost function which is defined as the computation core. In addition, using three clustering quality comparing criteria, the new developed method of differential evolutionary optimized of fuzzy cmeans - fuzzy covariance - fuzzy maximum likelihood estimation clustering method (DEF3) is compared with other base and common methods using field data. After doing the calculations, the developed method by giving the best values for all the criteria provided the best results and good stability in meeting different criteria. The DEF3 method was validated using actual field data which mapped in Rudbar Lorestan dam site. The results revealed that DEF3 acquired the best rank among the other method by getting the value of 0.5721 of Davis-Bouldin criterion, 1403.1 of Calinski-Harabasz criterion, and 0.83482 of Silihotte as comparing criteria of clustering methods

MRI Image Segmentation Using Active Contour and Fuzzy C-Means Algorithm

Interpretation of MRI images is difficult due to inherent noise and inhomogeneity. Segmentation is considered as vitally important step in medical image analysis and classification. Several methods are employed for medical image segmentation such as clustering method, thresholding method, region growing etc. In this paper, attention has been focused on clustering method such as Fuzzy C-means clustering algorithm that has been widely used for medical image segmentation. This algorithm was combined then with Active Contour method. Active Contours have been widely used as attractive image segmentation methods because they produce sub regions with continuous boundaries. The algorithms have been implemented and tested on MRI images. The comparison is made with existing conventional Fuzzy C-means method. Experimental results show that the proposed hybrid method significantly improves the Peak Signal to Noise Ratio (PSNR) and Mean Squared Error (MSE) for medical image segmentation