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Practical tools for clustering streaming data must be fast enough to handlethe arrival rate of the observations. Typically, they also must adapt on thefly to possible lack of stationarity; i.e., the data statistics may betime-dependent due to various forms of drifts, changes in the number ofclusters, etc. The Dirichlet Process Mixture Model (DPMM), whose Bayesiannonparametric nature allows it to adapt its complexity to the data, seems anatural choice for the streaming-data case. In its classical formulation,however, the DPMM cannot capture common types of drifts in the data statistics.Moreover, and regardless of that limitation, existing methods for online DPMMinference are too slow to handle rapid data streams. In this work we proposeadapting both the DPMM and a known DPMM sampling-based non-streaming inferencemethod for streaming-data clustering. We demonstrate the utility of theproposed method on several challenging settings, where it obtainsstate-of-the-art results while being on par with other methods in terms ofspeed.

Sampling in Dirichlet Process Mixture Models for Clustering Streaming Data