In this paper we address the problem of combining multiple clusterings without access to the underlying features of the data. This process is known in the literature as clustering...
Clustering aims at extracting hidden structure in dataset. While the problem of finding compact clusters has been widely studied in the literature, extracting arbitrarily formed ...
This paper presents a Distributed Efficient Clustering Approach (DECA) for mobility-resistant and energy-efficient clustering in multi-hop wireless networks. The clusterheads cover...
Given a unlabelled set of points X ∈ RN belonging to k groups, we propose a method to identify cluster assignments that provides maximum separating margin among the clusters. We...
Semi-supervised classification uses aspects of both unsupervised and supervised learning to improve upon the performance of traditional classification methods. Semi-supervised clu...