Density-based Projected Clustering over High Dimensional Data Streams

Clustering of high dimensional data streams is an important problem in many application domains, a prominent example being network monitoring. Several approaches have been lately proposed for solving independently the different aspects of the problem. There exist methods for clustering over full dimensional streams and methods for nding clusters in subspaces of high dimensional static data. Yet only a few approaches have been pro- posed so far which tackle both the stream and the high dimensionality aspects of the problem simultaneously. In this work, we propose a new density-based projected clustering algorithm, HDDStream, for high dimensional data streams. Our algorithm summarizes both the data points and the dimensions where these points are grouped together and maintains these summaries online, as new points arrive over time and old points expire due to ageing. Our experimental results illustrate the effectiveness and the eciency of HDDStream and also demonstrate that it could serve as a trigger for detecting drastic changes in the underlying stream population, like bursts of network attacks.