An efficient algorithm for mining large item sets

Hong Zhen Zheng; Dian Hui Chu; De Chen Zhan

An efficient algorithm for mining large item sets

Hong Zhen Zheng
, Dian Hui Chu
, De Chen Zhan

Faculty of Computing

School of Computer Science and Technology (School of Software), Harbin Institute of Technology Weihai

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

Abstract

It propose Online Mining Algorithm (OMA) which online discover large item sets. Without pre-setting a default threshold, the OMA algorithm achieves its efficiency and threshold-flexibility by calculating item-sets' counts. It is unnecessary and independent of the default threshold and can flexibly adapt to any user's input threshold. In addition, we propose Cluster-Based Association Rule Algorithm (CARA) creates cluster tables to aid discovery of large item sets. It only requires a single scan of the database, followed by contrasts with the partial cluster tables. It not only prunes considerable amounts of data reducing the time needed to perform data scans and requiring less contrast, but also ensures the correctness of the mined results. By using the CARA algorithm to create cluster tables in advance, each CPU can be utilized to process a cluster table; thus large item sets can be immediately mined even when the database is very large.

Original language	English
Title of host publication	CITSA 2006 - 3rd Int. Conf. on Cybernetics and Information Technol., Systems and Applications, Jointly with the 4th Int. Conf. on Computing, Communications and Control Technologies, CCCT 2006 - Proc.
Publisher	International Institute of Informatics and Systemics, IIIS
Pages	151-154
Number of pages	4
ISBN (Print)	9806560841, 9789806560840
State	Published - 2006
Event	3rd International Conference on Cybernetics and Information Technologies, Systems and Applications, CITSA 2006, Jointly with the 4th International Conference on Computing, Communications and Control Technologies, CCCT 2006 - Orlando, FL, United States Duration: 20 Jul 2006 → 23 Jul 2006

Publication series

Name	CITSA 2006 - 3rd Int. Conf. on Cybernetics and Information Technol., Systems and Applications, Jointly with the 4th Int. Conf. on Computing, Communications and Control Technologies, CCCT 2006 - Proc.
Volume	2

Conference

Conference	3rd International Conference on Cybernetics and Information Technologies, Systems and Applications, CITSA 2006, Jointly with the 4th International Conference on Computing, Communications and Control Technologies, CCCT 2006
Country/Territory	United States
City	Orlando, FL
Period	20/07/06 → 23/07/06

Keywords

Association rules
Data mining

Cite this

Zheng, H. Z., Chu, D. H., & Zhan, D. C. (2006). An efficient algorithm for mining large item sets. In CITSA 2006 - 3rd Int. Conf. on Cybernetics and Information Technol., Systems and Applications, Jointly with the 4th Int. Conf. on Computing, Communications and Control Technologies, CCCT 2006 - Proc. (pp. 151-154). (CITSA 2006 - 3rd Int. Conf. on Cybernetics and Information Technol., Systems and Applications, Jointly with the 4th Int. Conf. on Computing, Communications and Control Technologies, CCCT 2006 - Proc.; Vol. 2). International Institute of Informatics and Systemics, IIIS.

Zheng, Hong Zhen ; Chu, Dian Hui ; Zhan, De Chen. / An efficient algorithm for mining large item sets. CITSA 2006 - 3rd Int. Conf. on Cybernetics and Information Technol., Systems and Applications, Jointly with the 4th Int. Conf. on Computing, Communications and Control Technologies, CCCT 2006 - Proc.. International Institute of Informatics and Systemics, IIIS, 2006. pp. 151-154 (CITSA 2006 - 3rd Int. Conf. on Cybernetics and Information Technol., Systems and Applications, Jointly with the 4th Int. Conf. on Computing, Communications and Control Technologies, CCCT 2006 - Proc.).

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abstract = "It propose Online Mining Algorithm (OMA) which online discover large item sets. Without pre-setting a default threshold, the OMA algorithm achieves its efficiency and threshold-flexibility by calculating item-sets' counts. It is unnecessary and independent of the default threshold and can flexibly adapt to any user's input threshold. In addition, we propose Cluster-Based Association Rule Algorithm (CARA) creates cluster tables to aid discovery of large item sets. It only requires a single scan of the database, followed by contrasts with the partial cluster tables. It not only prunes considerable amounts of data reducing the time needed to perform data scans and requiring less contrast, but also ensures the correctness of the mined results. By using the CARA algorithm to create cluster tables in advance, each CPU can be utilized to process a cluster table; thus large item sets can be immediately mined even when the database is very large.",

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author = "Zheng, \{Hong Zhen\} and Chu, \{Dian Hui\} and Zhan, \{De Chen\}",

year = "2006",

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publisher = "International Institute of Informatics and Systemics, IIIS",

pages = "151--154",

booktitle = "CITSA 2006 - 3rd Int. Conf. on Cybernetics and Information Technol., Systems and Applications, Jointly with the 4th Int. Conf. on Computing, Communications and Control Technologies, CCCT 2006 - Proc.",

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Zheng, HZ, Chu, DH & Zhan, DC 2006, An efficient algorithm for mining large item sets. in CITSA 2006 - 3rd Int. Conf. on Cybernetics and Information Technol., Systems and Applications, Jointly with the 4th Int. Conf. on Computing, Communications and Control Technologies, CCCT 2006 - Proc.. CITSA 2006 - 3rd Int. Conf. on Cybernetics and Information Technol., Systems and Applications, Jointly with the 4th Int. Conf. on Computing, Communications and Control Technologies, CCCT 2006 - Proc., vol. 2, International Institute of Informatics and Systemics, IIIS, pp. 151-154, 3rd International Conference on Cybernetics and Information Technologies, Systems and Applications, CITSA 2006, Jointly with the 4th International Conference on Computing, Communications and Control Technologies, CCCT 2006, Orlando, FL, United States, 20/07/06.

An efficient algorithm for mining large item sets. / Zheng, Hong Zhen; Chu, Dian Hui ; Zhan, De Chen.
CITSA 2006 - 3rd Int. Conf. on Cybernetics and Information Technol., Systems and Applications, Jointly with the 4th Int. Conf. on Computing, Communications and Control Technologies, CCCT 2006 - Proc.. International Institute of Informatics and Systemics, IIIS, 2006. p. 151-154 (CITSA 2006 - 3rd Int. Conf. on Cybernetics and Information Technol., Systems and Applications, Jointly with the 4th Int. Conf. on Computing, Communications and Control Technologies, CCCT 2006 - Proc.; Vol. 2).

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

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N2 - It propose Online Mining Algorithm (OMA) which online discover large item sets. Without pre-setting a default threshold, the OMA algorithm achieves its efficiency and threshold-flexibility by calculating item-sets' counts. It is unnecessary and independent of the default threshold and can flexibly adapt to any user's input threshold. In addition, we propose Cluster-Based Association Rule Algorithm (CARA) creates cluster tables to aid discovery of large item sets. It only requires a single scan of the database, followed by contrasts with the partial cluster tables. It not only prunes considerable amounts of data reducing the time needed to perform data scans and requiring less contrast, but also ensures the correctness of the mined results. By using the CARA algorithm to create cluster tables in advance, each CPU can be utilized to process a cluster table; thus large item sets can be immediately mined even when the database is very large.

AB - It propose Online Mining Algorithm (OMA) which online discover large item sets. Without pre-setting a default threshold, the OMA algorithm achieves its efficiency and threshold-flexibility by calculating item-sets' counts. It is unnecessary and independent of the default threshold and can flexibly adapt to any user's input threshold. In addition, we propose Cluster-Based Association Rule Algorithm (CARA) creates cluster tables to aid discovery of large item sets. It only requires a single scan of the database, followed by contrasts with the partial cluster tables. It not only prunes considerable amounts of data reducing the time needed to perform data scans and requiring less contrast, but also ensures the correctness of the mined results. By using the CARA algorithm to create cluster tables in advance, each CPU can be utilized to process a cluster table; thus large item sets can be immediately mined even when the database is very large.

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SN - 9789806560840

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T2 - 3rd International Conference on Cybernetics and Information Technologies, Systems and Applications, CITSA 2006, Jointly with the 4th International Conference on Computing, Communications and Control Technologies, CCCT 2006

Y2 - 20 July 2006 through 23 July 2006

ER -

Zheng HZ, Chu DH , Zhan DC. An efficient algorithm for mining large item sets. In CITSA 2006 - 3rd Int. Conf. on Cybernetics and Information Technol., Systems and Applications, Jointly with the 4th Int. Conf. on Computing, Communications and Control Technologies, CCCT 2006 - Proc.. International Institute of Informatics and Systemics, IIIS. 2006. p. 151-154. (CITSA 2006 - 3rd Int. Conf. on Cybernetics and Information Technol., Systems and Applications, Jointly with the 4th Int. Conf. on Computing, Communications and Control Technologies, CCCT 2006 - Proc.).

An efficient algorithm for mining large item sets

Abstract

Publication series

Conference

Keywords

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