StreamSW: A density-based approach for clustering data streams over sliding windows

Abstract

Density-based clustering is a prominent and an essential technique in mining data streams. It can discover clusters of arbitrary or irregular shape and handle noisy data. This paper presents StreamSW, a new density-based approach for clustering streaming data over a sliding window (SW). A Sliding window is an extensively adopted window model for capturing and mining streaming data. The StreamSW approach adopts a two-phase framework for performing clustering on streaming data. In the online phase, the p-micro-clusters and grid structure are adopted to hold a synopsis of the streaming data. In the offline phase, final macro-clusters are created from the p-micro-clusters of online phase using the conventional DBSCAN algorithm. The StreamSW approach discovers the clusters with an irregular shape in limited memory and time. Experimental results exhibit that the speed and quality of StreamSW are better than the current approaches.