Text classification poses some specific challenges. One such challenge is its high dimensionality where each document (data point) contains only a small subset of them. In this pap...
Several application domains such as molecular biology and geography produce a tremendous amount of data which can no longer be managed without the help of efficient and effective ...
Abstract. Increasingly large multimedia databases in life sciences, ecommerce, or monitoring applications cannot be browsed manually, but require automatic knowledge discovery in d...
Subspace clustering is an extension of traditional clustering that seeks to find clusters in different subspaces within a dataset. This is a particularly important challenge with...
Data streams are often locally correlated, with a subset of streams exhibiting coherent patterns over a subset of time points. Subspace clustering can discover clusters of objects...
Subspace clustering (also called projected clustering) addresses the problem that different sets of attributes may be relevant for different clusters in high dimensional feature sp...
Given a data matrix, the problem of finding dense/uniform sub-blocks in the matrix is becoming important in several applications. The problem is inherently combinatorial since th...
We consider the problem of clustering data lying on multiple subspaces of unknown and possibly different dimensions. We show that one can represent the subspaces with a set of pol...
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 ...