Several real-world networks of interest, such as social and biological networks, are modular in nature. Most of these networks also possess the scale-free property, which makes the task of detecting and isolating communities from these networks difficult. The application of traditional clustering algorithms on these networks has not yielded a great deal of success. In this paper, we apply an en...

The combination of multiple clustering results (clustering ensemble) has emerged as an important procedure to improve the quality of clustering solutions. In this paper we propose a new cluster ensemble method based on kernel functions, which introduces the Partition Relevance Analysis step. This step has the goal of analyzing the set of partition in the cluster ensemble and extract valuable in...

Bagging and boosting are two successful well-known methods for developing classifier ensembles. It is recognized that the clusterer ensemble methods which utilize the boosting concept, can create clusterings with quality and robustness improvement. In this paper, we introduce a new boosting based hierarchical clusterer ensemble method called Bob-Hic. This method is utilized to create a consensu...

A key problem in visual tracking is how to handle the ambiguity in decision to locate the object effectively using the target appearance model with online update. We address this problem by incorporating sequential clustering and ensemble methods into the tracking system. In this paper, clustering is used for mining the potential historical structure in the parameter space and feature space. Th...

The emergence of high-dimensional data in various areas has brought new challenges to the ensemble clustering research. To deal with the curse of dimensionality, considerable efforts in ensemble clustering have been made by incorporating various subspace-based techniques. Besides the emphasis on subspaces, rather limited attention has been paid to the potential diversity in similarity/dissimila...

Ensemble clustering helps achieve fast under abundant computing resources by constructing multiple base clusterings. Compared with the standard single algorithm, ensemble integrates advantages of algorithms and has stronger robustness applicability. Nevertheless, most treat each result equally ignore difference clusters. If a cluster in is reliable/unreliable, it should play critical/uncritical...