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Granularity adaptive density estimation and on demand clustering of concept-drifting data streams

Publication ,  Conference
Zhu, W; Pei, J; Yin, J; Xie, Y
Published in: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
January 1, 2006

Clustering data streams has found a few important applications. While many previous studies focus on clustering objects arriving in a data stream, in this paper, we consider the novel problem of on demand clustering concept drifting data streams. In order to characterize concept drifting data streams, we propose an effective method to estimate densities of data streams. One unique feature of our new method is that its granularity of estimation is adaptive to the available computation resource, which is critical for processing data streams of unpredictable input rates. Moreover, we can apply any clustering method to on demand cluster data streams using their density estimations. A performance study on synthetic data sets is reported to verify our design, which clearly shows that our method obtains results comparable to CluStream [3] on clustering single stream, and much better results than COD [8] when clustering multiple streams. © Springer-Verlag Berlin Heidelberg 2006.

Duke Scholars

Published In

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

DOI

EISSN

1611-3349

ISSN

0302-9743

ISBN

9783540377368

Publication Date

January 1, 2006

Volume

4081 LNCS

Start / End Page

322 / 331

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 46 Information and computing sciences
 

Citation

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Zhu, W., Pei, J., Yin, J., & Xie, Y. (2006). Granularity adaptive density estimation and on demand clustering of concept-drifting data streams. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4081 LNCS, pp. 322–331). https://doi.org/10.1007/11823728_31
Zhu, W., J. Pei, J. Yin, and Y. Xie. “Granularity adaptive density estimation and on demand clustering of concept-drifting data streams.” In Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 4081 LNCS:322–31, 2006. https://doi.org/10.1007/11823728_31.
Zhu W, Pei J, Yin J, Xie Y. Granularity adaptive density estimation and on demand clustering of concept-drifting data streams. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2006. p. 322–31.
Zhu, W., et al. “Granularity adaptive density estimation and on demand clustering of concept-drifting data streams.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4081 LNCS, 2006, pp. 322–31. Scopus, doi:10.1007/11823728_31.
Zhu W, Pei J, Yin J, Xie Y. Granularity adaptive density estimation and on demand clustering of concept-drifting data streams. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2006. p. 322–331.
Journal cover image

Published In

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

DOI

EISSN

1611-3349

ISSN

0302-9743

ISBN

9783540377368

Publication Date

January 1, 2006

Volume

4081 LNCS

Start / End Page

322 / 331

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 46 Information and computing sciences