Collective principal component analysis from distributed, heterogeneous data

Hillol Kargupta; Weiyun Huang; Krishnamoorthy Sivakumar; Byung Hoon Park; Shuren Wang

doi:10.1007/3-540-45372-5_50

Collective principal component analysis from distributed, heterogeneous data

Hillol Kargupta
, Weiyun Huang
, Krishnamoorthy Sivakumar
, Byung Hoon Park
, Shuren Wang

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

30 Scopus citations

Abstract

Principal component analysis (PCA) is a statistical technique to identify the dependency structure of multivariate stochastic observations. PCA is frequently used in data mining applications. This paper considers PCA in the context of the emerging network-based computing environments. It offers a technique to perform PCA from distributed and heterogeneous data sets with relatively small communication overhead. The technique is evaluated against different data sets, including a data set for a web mining application. This approach is likely to facilitate the development of distributed clustering, associative link analysis, and other heterogeneous data mining applications that frequently use PCA.

Original language	English
Title of host publication	Principles of Data Mining and Knowledge Discovery - 4th European Conference, PKDD 2000, Proceedings
Editors	Djamel A. Zighed, Jan Komorowski, Jan Zytkow
Publisher	Springer Verlag
Pages	452-457
Number of pages	6
ISBN (Print)	9783540410669
DOIs	https://doi.org/10.1007/3-540-45372-5_50
State	Published - 2000
Externally published	Yes
Event	4th European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD 2000 - Lyon, France Duration: Sep 13 2000 → Sep 16 2000

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	1910
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	4th European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD 2000
Country/Territory	France
City	Lyon
Period	09/13/00 → 09/16/00

Funding

The authors thank American Cancer Society for supporting part of this research.

Access to Document

10.1007/3-540-45372-5_50

Cite this

Kargupta, H., Huang, W., Sivakumar, K., Park, B. H., & Wang, S. (2000). Collective principal component analysis from distributed, heterogeneous data. In D. A. Zighed, J. Komorowski, & J. Zytkow (Eds.), Principles of Data Mining and Knowledge Discovery - 4th European Conference, PKDD 2000, Proceedings (pp. 452-457). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1910). Springer Verlag. https://doi.org/10.1007/3-540-45372-5_50

Kargupta, Hillol ; Huang, Weiyun ; Sivakumar, Krishnamoorthy et al. / Collective principal component analysis from distributed, heterogeneous data. Principles of Data Mining and Knowledge Discovery - 4th European Conference, PKDD 2000, Proceedings. editor / Djamel A. Zighed ; Jan Komorowski ; Jan Zytkow. Springer Verlag, 2000. pp. 452-457 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Collective principal component analysis from distributed, heterogeneous data",

abstract = "Principal component analysis (PCA) is a statistical technique to identify the dependency structure of multivariate stochastic observations. PCA is frequently used in data mining applications. This paper considers PCA in the context of the emerging network-based computing environments. It offers a technique to perform PCA from distributed and heterogeneous data sets with relatively small communication overhead. The technique is evaluated against different data sets, including a data set for a web mining application. This approach is likely to facilitate the development of distributed clustering, associative link analysis, and other heterogeneous data mining applications that frequently use PCA.",

author = "Hillol Kargupta and Weiyun Huang and Krishnamoorthy Sivakumar and Park, \{Byung Hoon\} and Shuren Wang",

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Kargupta, H, Huang, W, Sivakumar, K, Park, BH & Wang, S 2000, Collective principal component analysis from distributed, heterogeneous data. in DA Zighed, J Komorowski & J Zytkow (eds), Principles of Data Mining and Knowledge Discovery - 4th European Conference, PKDD 2000, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 1910, Springer Verlag, pp. 452-457, 4th European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD 2000, Lyon, France, 09/13/00. https://doi.org/10.1007/3-540-45372-5_50

Collective principal component analysis from distributed, heterogeneous data. / Kargupta, Hillol; Huang, Weiyun; Sivakumar, Krishnamoorthy et al.
Principles of Data Mining and Knowledge Discovery - 4th European Conference, PKDD 2000, Proceedings. ed. / Djamel A. Zighed; Jan Komorowski; Jan Zytkow. Springer Verlag, 2000. p. 452-457 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1910).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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N2 - Principal component analysis (PCA) is a statistical technique to identify the dependency structure of multivariate stochastic observations. PCA is frequently used in data mining applications. This paper considers PCA in the context of the emerging network-based computing environments. It offers a technique to perform PCA from distributed and heterogeneous data sets with relatively small communication overhead. The technique is evaluated against different data sets, including a data set for a web mining application. This approach is likely to facilitate the development of distributed clustering, associative link analysis, and other heterogeneous data mining applications that frequently use PCA.

AB - Principal component analysis (PCA) is a statistical technique to identify the dependency structure of multivariate stochastic observations. PCA is frequently used in data mining applications. This paper considers PCA in the context of the emerging network-based computing environments. It offers a technique to perform PCA from distributed and heterogeneous data sets with relatively small communication overhead. The technique is evaluated against different data sets, including a data set for a web mining application. This approach is likely to facilitate the development of distributed clustering, associative link analysis, and other heterogeneous data mining applications that frequently use PCA.

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Kargupta H, Huang W, Sivakumar K, Park BH, Wang S. Collective principal component analysis from distributed, heterogeneous data. In Zighed DA, Komorowski J, Zytkow J, editors, Principles of Data Mining and Knowledge Discovery - 4th European Conference, PKDD 2000, Proceedings. Springer Verlag. 2000. p. 452-457. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/3-540-45372-5_50

Collective principal component analysis from distributed, heterogeneous data

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