Real-World networks have an inherently dynamic structure and are often composed of communities that are constantly changing in membership. Identifying these communities is of great importance when analyzing structural properties of networks. Hence, recent years have witnessed intense research in of solving the challenging problem of detecting such evolving communities. The mainstream approach t...

Wireless sensor networks are an emerging technology for monitoring physical domain. The energy limitation of wireless sensor networks makes energy sparing and augmenting the network lifetime turn into the most essential objectives of different routing protocols. Heterogeneous wireless sensor network (WSN) comprises of sensor nodes with distinctive capability, for example, diverse computing powe...

We consider the ensemble clustering problem where the task is to 'aggregate' multiple clustering solutions into a single consolidated clustering that maximizes the shared information among given clustering solutions. We obtain several new results for this problem. First, we note that the notion of agreement under such circumstances can be better captured using an agreement measure based on a 2D...

Cluster analysis has received attention in many scientific fields. The purpose of clustering analysis is to detect group data points, which are close to one another. One of the most widely used techniques for clustering is the K-means algorithm. The performance of K-means algorithm which converges to numerous local minima depends highly on initial cluster centers. In order to overcome local opt...

A genetic approach is developed, which is suitable for the optimization of fuzzy c-means clustering. The approach is based on real encoding of the prototype variables (cluster centers) and uses appropriate genetic operators and techniques to optimize the clustering criterion. Experimental results concerning diicult clustering problems show that the proposed approach is very successful in genera...

In this paper, we explore the discriminating subsequencebased clustering problem. First, several effective optimization techniques are proposed to accelerate the sequence mining process and a new algorithm, CONTOUR, is developed to efficiently and directly mine a subset of discriminating frequent subsequences which can be used to cluster the input sequences. Second, an accurate hierarchical clu...

Partitioning of an image into several constituent components is called image segmentation. Myriad algorithms using different methods have been proposed for image segmentation. Many clustering algorithms and optimization techniques are also being used for segmentation of images. A major challenge in segmentation evaluation comes from the fundamental conflict between generality and objectivity. A...

In this paper, a systematic optimization approach for clustering proximity or similarity data is developed. Starting from fundamental invariance and robustness properties, a set of axioms is proposed and discussed to distinguish di erent cluster compactness and separation criteria. The approach covers the case of sparse proximity matrices, and is extended to nested partitionings for hierarchica...

We have proposed tolerant fuzzy c-means clustering (TFCM) from the viewpoint of handling data more flexibly. This paper presents a new type of tolerant fuzzy c-means clustering with L1-regularization. L1-regularization is wellknown as the most successful techniques to induce sparseness. The proposed algorithm is different from the viewpoint of the sparseness for tolerance vector. In the origina...

energy consumption is considered as a critical issue in wireless sensor networks (wsns). batteries of sensor nodes have limited power supply which in turn limits services and applications that can be supported by them. an efcient solution to improve energy consumption and even trafc in wsns is data aggregation (da) that can reduce the number of transmissions. two main challenges for da are: (i)...