In this paper, we present a measure associated with detection and inference of statistically anomalous clusters of a graph based on the likelihood test of observed and expected ed...
Bei Wang, Jeff M. Phillips, Robert Schreiber, Denn...
The performance of document clustering systems depends on employing optimal text representations, which are not only difficult to determine beforehand, but also may vary from one ...
Network clustering (or graph partitioning) is an important task for the discovery of underlying structures in networks. Many algorithms find clusters by maximizing the number of i...
Xiaowei Xu, Nurcan Yuruk, Zhidan Feng, Thomas A. J...
Existing data-stream clustering algorithms such as CluStream are based on k-means. These clustering algorithms are incompetent to find clusters of arbitrary shapes and cannot hand...
A novel breadth-first based structural clustering method for graphs is proposed. Clustering is an important task for analyzing complex networks such as biological networks, World ...