Anomaly detection in high dimensional data is becoming a fundamental research problem
that has various applications in the real world. However, many existing anomaly detection …

High‐dimensional data in Euclidean space pose special challenges to data mining
algorithms. These challenges are often indiscriminately subsumed under the term 'curse of …

Robust subspace clustering Page 1 The Annals of Statistics 2014, Vol. 42, No. 2, 669–699
DOI: 10.1214/13-AOS1199 © Institute of Mathematical Statistics, 2014 ROBUST …

This paper represents another step in overcoming a drawback of K-Means, its lack of
defense against noisy features, using feature weights in the criterion. The Weighted K …

In this paper, we present a hybrid recommendation approach for discovering potential
preferences of individual users. The proposed approach provides a flexible solution that …

Given a graph with node attributes, what neighborhoods are anomalous? To answer this
question, one needs a quality score that utilizes both structure and attributes. Popular …

High‐dimensional data, ie, data described by a large number of attributes, pose specific
challenges to clustering. The so‐called 'curse of dimensionality', coined originally to …

High-dimensional data arise naturally in many domains, and have regularly presented a
great challenge for traditional data mining techniques, both in terms of effectiveness and …

Subspace clustering finds sets of objects that are homogeneous in subspaces of high-
dimensional datasets, and has been successfully applied in many domains. In recent years …

How can we find the right graph for semi-supervised learning? In real world applications, the
choice of which edges to use for computation is the first step in any graph learning process …