Clustering is the unsupervised classification of patterns (observations, data items, or feature
vectors) into groups (clusters). The clustering problem has been addressed in many contexts …

When working on systems of the real world, abstractions in the form of graphs have proven a
superior modeling and representation approach. This paper is on the analysis of such …

We have developed a novel algorithm for cluster analysis that is based on graph theoretic
techniques. A similarity graph is defined and clusters in that graph correspond to highly …

We present a stochastic clustering algorithm which uses pairwise similarity of elements,
based on a new graph theoretical algorithm for the sampling of cuts in graphs. The …

Segmentation is the low-level operation in image processing which partitions an image into
disjoint and homogeneous regions. We consider segmentation of color images, a task which …

Image segmentation partitions a digital image into disjoint regions, each region is
homogeneous, while adjacent regions are not. A variety of methods have been used to …

This paper introduces a simple new approach to object recognition from silhouettes. This
new approach utilizes features extracted using an adaptive approximation technique called …

Robust high-breakdown-point location estimators are employed to analyze image stacks
under the piecewise constant image structure model. To reduce the effect of bias along the Z …

We study a group of clustering problems on bipartite and chordal graphs. Our objective is to
partition the vertices of a graph into a restricted number of sets so that a prespecified …