We present a principled spectral approach to the wellstudied constrained clustering problem. It reduces clustering to a generalized eigenvalue problem on Laplacians. The method works in nearly-linear time and provides concrete guarantees for the quality of the clusters, at least for the case of 2-way partitioning. In practice this translates to a very fast implementation that consistently outpe...

We suggest using the max-norm as a convex surrogate constraint for clustering. We show how this yields a better exact cluster recovery guarantee than previously suggested nuclear-norm relaxation, and study the effectiveness of our method, and other related convex relaxations, compared to other clustering approaches.

The information for many important problems can be found in various formats and modalities. Besides standard tabular form, these include also text and graphs. To solve such problems fusion of different data sources is required. We demonstrate a methodology which is capable to enrich textual information with graph based data and utilize both in an innovative machine learning application of clust...

Recently, clustering moving object trajectories kept gaining interest from both the data mining and machine learning communities. This problem, however, was studied mainly and extensively in the setting where moving objects can move freely on the euclidean space. In this paper, we study the problem of clustering trajectories of vehicles whose movement is restricted by the underlying road networ...

We present a survey of the basic mechanisms and properties of existing clustering algorithms for wireless ad hoc networks. Based on this evaluation, we then propose a new algorithm with improved stability and a lower communication overhead. This is partly achieved by using a maintenance function that modifies the existing clustering structure rather than building a new one from scratch. Prelimi...