Sampling in Dirichlet Process Mixture Models for Clustering Streaming Data
This work addresses the need for fast and adaptive clustering tools in streaming data applications, though it is incremental as it builds on existing DPMM methods.
The authors tackled the problem of clustering streaming data with non-stationary statistics by adapting the Dirichlet Process Mixture Model and a sampling-based inference method for online use, achieving state-of-the-art results in challenging settings while maintaining competitive speed.
Practical tools for clustering streaming data must be fast enough to handle the arrival rate of the observations. Typically, they also must adapt on the fly to possible lack of stationarity; i.e., the data statistics may be time-dependent due to various forms of drifts, changes in the number of clusters, etc. The Dirichlet Process Mixture Model (DPMM), whose Bayesian nonparametric nature allows it to adapt its complexity to the data, seems a natural 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 DPMM inference are too slow to handle rapid data streams. In this work we propose adapting both the DPMM and a known DPMM sampling-based non-streaming inference method for streaming-data clustering. We demonstrate the utility of the proposed method on several challenging settings, where it obtains state-of-the-art results while being on par with other methods in terms of speed.