Most clustering algorithms are partitional in nature, assigning each data point to exactly one cluster. However, several real world datasets have inherently overlapping clusters i...
The identification of clusters, well-connected components in a graph, is useful in many applications from biological function prediction to social community detection. However, ...
The paper introduces a framework for clustering data objects in a similarity-based context. The aim is to cluster objects into a given number of classes without imposing a hard pa...
Since a large number of clustering algorithms exist, aggregating different clustered partitions into a single consolidated one to obtain better results has become an important pro...
—Analysis and modeling of wireless networks greatly depend on understanding the structure of underlying mobile nodes. In this paper we present two clustering algorithms to determ...
Yung-Chih Chen, Elisha J. Rosensweig, Jim Kurose, ...