[1] Zou Lingjun, Chen Ling, Tu Li, et al. Clustering algorithm for multiple data streamsbased on spectral component similarity [J]. Journal of Southeast University (English Edition), 2008, 24 (3): 264-266. [doi:10.3969/j.issn.1003-7985.2008.03.003]

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Clustering algorithm for multiple data streamsbased on spectral component similarity()

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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

24

Issue:

2008 3

Page:

264-266

Research Field:

Computer Science and Engineering

Publishing date:

2008-09-30

Info

Title:

Clustering algorithm for multiple data streamsbased on spectral component similarity

Author(s):

Zou Lingjun¹;  Chen Ling¹;  2;  Tu Li³

¹Information Engineering College, Yangzhou University, Yangzhou 225009, China
²State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210093, China
³College of Information Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China

Keywords:

data streams;  clustering;  AR model;  spectral component

PACS:

TP311

DOI:

10.3969/j.issn.1003-7985.2008.03.003

Abstract:

A new algorithm for clustering multiple data streams is proposed.The algorithm can effectively cluster data streams which show similar behavior with some unknown time delays.The algorithm uses the autoregressive(AR)modeling technique to measure correlations between data streams.It exploits estimated frequencies spectra to extract the essential features of streams.Each stream is represented as the sum of spectral components and the correlation is measured component-wise.Each spectral component is described by four parameters, namely, amplitude, phase, damping rate and frequency.The ε-lag-correlation between two spectral components is calculated.The algorithm uses such information as similarity measures in clustering data streams.Based on a sliding window model, the algorithm can continuously report the most recent clustering results and adjust the number of clusters.Experiments on real and synthetic streams show that the proposed clustering method has a higher speed and clustering quality than other similar methods.

References:

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Memo

Memo:

Biographies: Zou Lingjun(1984—), female, graduate;Chen Ling(corresponding author), male, professor, lchen@yzcn.net.
Foundation items: The National Natural Science Foundation of China(No.60673060), the Natural Science Foundation of Jiangsu Province(No.BK2005047).
Citation: Zou Lingjun, Chen Ling, Tu Ii.Clustering algorithm for multiple data streams based on spectral component similarity[J].Journal of Southeast University(English Edition), 2008, 24(3):264-266.

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