A clustering algorithm for multiple data streams based on spectral component similarity

作者：Chen Ling^*; Zou Ling-Jun; Tu Li

来源：Information Sciences, 2012, 183(1): 35-47.

DOI：10.1016/j.ins.2011.09.004

• Data streams
• Clustering
• Auto-regression model
• Spectral component

We propose a new algorithm to cluster multiple and parallel data streams using spectral component similarity analysis, a new similarity metric. This new algorithm can effectively cluster data streams that show similar behaviour to each other but with unknown time delays. The algorithm performs auto-regressive modelling to measure the lag correlation between the data streams and uses it as the distance metric for clustering. The algorithm uses a sliding window model to continuously report the most recent clustering results and to dynamically adjust the number of clusters. Our experimental results on real and synthetic datasets show that our algorithm has better clustering quality, efficiency, and stability than other existing methods.

• 出版日期2012-1-15
• 单位扬州大学; 南京大学