Traditional image change detection based on a non-subsampled contourlet transform always ignores the neighborhood information's relationship to the non-subsampled contourlet coefficients, and the detection results are susceptible to noise interference. To address these disadvantages, we propose a denoising method based on the non-subsampled contourlet transform domain that uses the Hidden Marko...

Fuzzy C-Means (FCM) is a common data analysis method, but the clustering effect of this algorithm easily affected by initial centers. Currently, scholars often use multiple population genetic (MPGA) to optimize centers, MPGA has insufficient global search ability and lacks self-adaptability, prone premature convergence, poor Therefore, paper proposes an adaptive FCM DMGA-FCM based on derivative...

A quality of centroid-based clustering is highly dependent on initialization. In the article we propose initialization based on the probability of finding objects, which could represent individual clusters. We present results of experiments which compare the quality of clustering obtained by k-means algorithm and by selected methods for fuzzy clustering: FCM (fuzzy c-means), PCA (possibilistic ...

In this study, an efficient method is introduced to predict the stability of soil-structure interaction (SSI) system subject to earthquake loads. In the procedure of the nonlinear dynamic analysis, a number of structures collapse and then lose their stability. The prediction of failure probability is considered as stability criterion. In order to achieve this purpose, a modified adaptive neuro ...

Unsupervised competitive learning algorithms for clustering of sensor nodes in wireless sensor networks are evaluated with a large scale data set in this paper. The Centroid Neural Network (CNN) is compared with Fuzzy c-Means (FCM) algorithm in determining cluster heads among given sensor nodes. The cluster heads are combined with Low Energy Adaptive Clustering Hierarchy (LEACH) for minimizing ...

Detection and segmentation of Brain tumor is very important because it provides anatomical information of normal and abnormal tissues which helps in treatment planning and patient follow-up. There are number of techniques for image segmentation. Proposed research work uses ANFIS (Artificial Neural Network Fuzzy Inference System) for image classification and then compares the results with FCM (F...

This paper explains the approximation of a membership function obtained by entropy regularization of the fuzzy c-means (FCM) method. By regularizing FCM with fuzzy entropy, a membership function similar to the Fermi-Dirac distribution function is obtained. We propose a new clustering method, in which the minimum of the Helmholtz free energy for FCM is searched by deterministic annealing (DA), w...

Fuzzy C-means (FCM) is a popular algorithm using the partitioning approach to solve problems in data clustering. A drawback to FCM, however, is that it requires the number of clusters and the clustering partition matrix to be set a priori. Typically, the former is set by the user and the latter is initialized randomly. This approach may cause the algorithm get stuck in a local optimum because F...

Clustering is a popular data analysis and data mining technique. In this paper, a novel chaotic particle swarm fuzzy clustering (CPSFC) algorithm based on chaotic particle swarm (CPSO) and gradient method is proposed. Fuzzy clustering model optimization is challenging, in order to solve this problem, adaptive inertia weight factor (AIWF) and iterative chaotic map with infinite collapses (ICMIC)...