A new clustering algorithm for transaction data via caucus

Jinmei Xu; Hui Xiong; Sam Yuan Sung; Vipin Kumar

doi:10.1007/3-540-36175-8_55

A new clustering algorithm for transaction data via caucus

Jinmei Xu, Hui Xiong, Sam Yuan Sung, Vipin Kumar

Computer Science and Engineering

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

4 Scopus citations

Abstract

The fast-growing large point of sale databases in stores and companies sets a pressing need for extracting high-level knowledge. Transaction clustering arises to receive attentions in recent years. However, traditional clustering techniques are not useful to solve this problem. Transaction data sets are different from the traditional data sets in their high dimensionality, sparsity and a large number of outliers. In this paper we present and experimentally evaluate a new efficient transaction clustering technique based on cluster of buyers called caucus that can be effectively used for identification of center of cluster. Experiments on real and synthetic data sets indicate that compare to prior work, caucus-based method can derive clusters of better quality as well as reduce the execution time considerably.

Original language	English (US)
Title of host publication	Advances in Knowledge Discovery and Data Mining
Editors	Kyu-Young Wang, Jongwoo Jeon, Kyuseok Shim, Jaideep Srivastava
Publisher	Springer Verlag
Pages	551-562
Number of pages	12
ISBN (Electronic)	3540047603, 9783540047605
DOIs	https://doi.org/10.1007/3-540-36175-8_55
State	Published - 2003
Event	7th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2003 - Seoul, Korea, Republic of Duration: Apr 30 2003 → May 2 2003

Publication series

Name	Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)
Volume	2637
ISSN (Print)	0302-9743

Other

Other	7th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2003
Country/Territory	Korea, Republic of
City	Seoul
Period	4/30/03 → 5/2/03

Bibliographical note

Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 2003.

Publisher link

10.1007/3-540-36175-8_55

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Xu, J., Xiong, H., Sung, S. Y., & Kumar, V. (2003). A new clustering algorithm for transaction data via caucus. In K-Y. Wang, J. Jeon, K. Shim, & J. Srivastava (Eds.), Advances in Knowledge Discovery and Data Mining (pp. 551-562). (Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science); Vol. 2637). Springer Verlag. https://doi.org/10.1007/3-540-36175-8_55

A new clustering algorithm for transaction data via caucus. / Xu, Jinmei; Xiong, Hui; Sung, Sam Yuan et al.

Advances in Knowledge Discovery and Data Mining. ed. / Kyu-Young Wang; Jongwoo Jeon; Kyuseok Shim; Jaideep Srivastava. Springer Verlag, 2003. p. 551-562 (Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science); Vol. 2637).

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

Xu, J, Xiong, H, Sung, SY & Kumar, V 2003, A new clustering algorithm for transaction data via caucus. in K-Y Wang, J Jeon, K Shim & J Srivastava (eds), Advances in Knowledge Discovery and Data Mining. Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), vol. 2637, Springer Verlag, pp. 551-562, 7th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2003, Seoul, Korea, Republic of, 4/30/03. https://doi.org/10.1007/3-540-36175-8_55

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abstract = "The fast-growing large point of sale databases in stores and companies sets a pressing need for extracting high-level knowledge. Transaction clustering arises to receive attentions in recent years. However, traditional clustering techniques are not useful to solve this problem. Transaction data sets are different from the traditional data sets in their high dimensionality, sparsity and a large number of outliers. In this paper we present and experimentally evaluate a new efficient transaction clustering technique based on cluster of buyers called caucus that can be effectively used for identification of center of cluster. Experiments on real and synthetic data sets indicate that compare to prior work, caucus-based method can derive clusters of better quality as well as reduce the execution time considerably.",

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A new clustering algorithm for transaction data via caucus

Abstract

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