Clustering, in data mining, is useful to discover distribution patterns in the underlying data. Clustering algorithms usually employ a distance metric based (e.g., Euclidean) similarity measure in order to partition the database such that data points in the same partition are more similar than points in different partitions. The problem of clustering becomes more challenging when the data is ca...

Recent ensemble clustering techniques have been shown to be effective in improving the accuracy and stability of standard clustering algorithms. However, an inherent drawback of these techniques is the computational cost of generating and combining multiple clusterings of the data. In this paper, we present an efficient kernel-based ensemble clustering method suitable for application to large, ...

Received Apr 18, 2017 Revised May 30, 2017 Accepted Jun 15, 2017 A social network is indeed an abstraction of related groups interacting amongst themselves to develop relationships. However, toanalyze any relationships and psychology behind it, clustering plays a vital role. Clustering enhances the predictability and discoveryof like mindedness amongst users. This article’s goal exploits the te...

The aim of clustering ensemble is to combine multiple base partitions into a robust, stable and accurate partition. One of the key problems of clustering ensemble is how to exploit the cluster structure information in each base partition. Evidence accumulation is an effective framework which can convert the base partitions into a co-association matrix. This matrix describes the frequency of a p...

This paper explores the problem of clustering ensemble, which aims to combine multiple base clusterings produce better performance than that individual one. The existing ensemble methods generally construct a co-association matrix, indicates pairwise similarity between samples, as weighted linear combination connective matrices from different clusterings, and resulting matrix is then adopted in...