This paper introduces a system, called PolyCluster, which adopts state-of-the-art algorithms for data visualization and integrates human domain knowledge into the construction pro...
We study a general algorithm to improve accuracy in cluster analysis that employs the James-Stein shrinkage effect in k-means clustering. We shrink the centroids of clusters towar...
In this paper we study the k-means clustering problem. It is well-known that the general version of this problem is NP-hard. Numerous approximation algorithms have been proposed fo...
Bi-clustering is a promising conceptual clustering approach. Within categorical data, it provides a collection of (possibly overlapping) bi-clusters, i.e., linked clusters for both...
Subspace clustering has many applications in computer vision, such as image/video segmentation and pattern classification. The major issue in subspace clustering is to obtain the ...