Energy constraint is the biggest challenge in wireless sensor networks because the power supply of each sensor node is a battery that is not rechargeable or replaceable due to the applications of these networks. One of the successful methods for saving energy in these networks is clustering. It has caused that cluster-based routing algorithms are successful routing algorithm for these networks....

In this work, a hierarchical ensemble of projected clustering algorithm for high-dimensional data is proposed. The basic concept of the algorithm is based on the active learning method (ALM) which is a fuzzy learning scheme, inspired by some behavioral features of human brain functionality. High-dimensional unsupervised active learning method (HUALM) is a clustering algorithm which blurs the da...

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...

Graphs have so many applications in real world problems. When we deal with huge volume of data, analyzing data is difficult or sometimes impossible. In big data problems, clustering data is a useful tool for data analysis. Singular value decomposition(SVD) is one of the best algorithms for clustering graph but we do not have any choice to select the number of clusters and the number of members ...

In this paper, the development of an improved Takagi Sugeno (TS) fuzzy model for identification and chaotic time series prediction of nonlinear dynamical systems is proposed. This model combines the advantages of fuzzy systems and Infinite Impulse Response (IIR) filters, which are autoregressive moving average models, to create internal dynamics with just the control input. The structure of Fuz...

In this paper wk have used two fuzzy clustering algorithms, namely Fuzzy C-Means (FCM) and Gustafson Kessel Clustering (GKC) for unsupervised change detection in multitemporal remote sensing images. In conventional FCM & GKC no spatio-contextual information is taken into account and thus the result is not so much robust to noise/outliers. By incorporation of local neighborhood informationthe pe...

An algorithm to classify the network traffic based on improved support vector machine (SVM) is presented in this paper. Each feature of the traditional support vector machine (SVM) algorithm has the same effect on classification rather than considering its practical effect. To improve the classification accuracy of SVM, the probabilistic distributing area of a feature in a kind of network traff...