Clustering Evolving Data Streams over Sliding Windows

Abstract

To address the sliding window based clustering, two types of exponential histogram of cluster features, false positive and false negative, are introduced in this paper. With these structures, a clustering algorithm based on sliding windows is proposed. The algorithm can precisely obtain the distribution of recent records with limited memory, thus it can produce the clustering result over sliding windows. Furthermore, it can be extended to deal with the clustering problem over N-n window (an extended model of the sliding window). The evolving data streams in the experiments include KDD-CUP’99 and KDD-CUP’98 real data sets and synthetic data sets with changing Gaussian distribution. Theoretical analysis and comprehensive experimental results demonstrate that the proposed method is of high quality, little memory and fast processing rate.