A MPAA-Based iterative clustering algorithm augmented by nearest neighbors search for time-series data streams

Authors:
Jessica Lin;Michai Vlachos;Eamonn Keogh;Dimitrios Gunopulos;Jianwei Liu;Shoujian Yu;Jiajin Le
Affiliations:
Department of Computer Science and Engineering, University of California, Riverside, Riverside, CA;Department of Computer Science and Engineering, University of California, Riverside, Riverside, CA;Department of Computer Science and Engineering, University of California, Riverside, Riverside, CA;Department of Computer Science and Engineering, University of California, Riverside, Riverside, CA;College of Computer Science & Technology, Donghua University;College of Computer Science & Technology, Donghua University;College of Computer Science & Technology, Donghua University
Venue:
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Year:
2005

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Abstract

In streaming time series the Clustering problem is more complex, since the dynamic nature of streaming data makes previous clustering methods inappropriate. In this paper, we propose firstly a new method to evaluate Clustering in streaming time series databases. First, we introduce a novel multi-resolution PAA (MPAA) transform to achieve our iterative clustering algorithm. The method is based on the use of a multi-resolution piecewise aggregate approximation representation, which is used to extract features of time series. Then, we propose our iterative clustering approach for streaming time series. We take advantage of the multiresolution property of MPPA and equip a stopping criteria based on Hoeffding bound in order to achieve fast response time. Our streaming time-series clustering algorithm also works by leveraging off the nearest neighbors of the incoming streaming time series datasets and fulfill incremental clustering approach. The comprehensive experiments based on several publicly available real data sets shows that significant performance improvement is achieved and produce high-quality clusters in comparison to the previous methods.