Efficiently mining high average utility itemsets with a tree structure

Chun Wei Lin; Tzung Pei Hong; Wen-Hsiang Lu

doi:10.1007/978-3-642-12145-6_14

Efficiently mining high average utility itemsets with a tree structure

Chun Wei Lin, Tzung Pei Hong, Wen-Hsiang Lu

資訊工程學系

研究成果: Conference contribution

66 引文斯高帕斯（Scopus）

摘要

The average utility measure has recently been proposed to reveal a better utility effect of combining several items than the original utility measure. It is defined as the total utility of an itemset divided by its number of items within it. In this paper, a new mining approach with the aid of a tree structure is proposed to efficiently implement the concept. The high average utility pattern tree (HAUP tree) structure is first designed to help keep some related information and then the HAUP-growth algorithm is proposed to mine high average utility itemsets from the tree structure. Experimental results also show that the proposed approach has a better performance than the Apriori-like average utility mining.

原文	English
主出版物標題	Intelligent Information and Database Systems - Second International Conference, ACIIDS, Proceedings
頁面	131-139
頁數	9
版本	PART 1
DOIs	https://doi.org/10.1007/978-3-642-12145-6_14
出版狀態	Published - 2010 9月 29
事件	2010 Asian Conference on Intelligent Information and Database Systems, ACIIDS 2010 - Hue City, Viet Nam 持續時間: 2010 3月 24 → 2010 3月 26

出版系列

名字	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
號碼	PART 1
卷	5990 LNAI
ISSN（列印）	0302-9743
ISSN（電子）	1611-3349

Other

Other	2010 Asian Conference on Intelligent Information and Database Systems, ACIIDS 2010
國家/地區	Viet Nam
城市	Hue City
期間	10-03-24 → 10-03-26

All Science Journal Classification (ASJC) codes

理論電腦科學
一般電腦科學

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10.1007/978-3-642-12145-6_14

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引用此

Lin, C. W., Hong, T. P., & Lu, W.-H. (2010). Efficiently mining high average utility itemsets with a tree structure. 於 Intelligent Information and Database Systems - Second International Conference, ACIIDS, Proceedings (PART 1 編輯, 頁 131-139). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); 卷 5990 LNAI, 編號 PART 1). https://doi.org/10.1007/978-3-642-12145-6_14

Lin, Chun Wei ; Hong, Tzung Pei ; Lu, Wen-Hsiang. / Efficiently mining high average utility itemsets with a tree structure. Intelligent Information and Database Systems - Second International Conference, ACIIDS, Proceedings. PART 1. 編輯 2010. 頁 131-139 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1).

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abstract = "The average utility measure has recently been proposed to reveal a better utility effect of combining several items than the original utility measure. It is defined as the total utility of an itemset divided by its number of items within it. In this paper, a new mining approach with the aid of a tree structure is proposed to efficiently implement the concept. The high average utility pattern tree (HAUP tree) structure is first designed to help keep some related information and then the HAUP-growth algorithm is proposed to mine high average utility itemsets from the tree structure. Experimental results also show that the proposed approach has a better performance than the Apriori-like average utility mining.",

author = "Lin, {Chun Wei} and Hong, {Tzung Pei} and Wen-Hsiang Lu",

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Lin, CW, Hong, TP & Lu, W-H 2010, Efficiently mining high average utility itemsets with a tree structure. 於 Intelligent Information and Database Systems - Second International Conference, ACIIDS, Proceedings. PART 1 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 編號 PART 1, 卷 5990 LNAI, 頁 131-139, 2010 Asian Conference on Intelligent Information and Database Systems, ACIIDS 2010, Hue City, Viet Nam, 10-03-24. https://doi.org/10.1007/978-3-642-12145-6_14

Efficiently mining high average utility itemsets with a tree structure. / Lin, Chun Wei; Hong, Tzung Pei; Lu, Wen-Hsiang.
Intelligent Information and Database Systems - Second International Conference, ACIIDS, Proceedings. PART 1. 編輯 2010. p. 131-139 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); 卷 5990 LNAI, 編號 PART 1).

研究成果: Conference contribution

TY - GEN

T1 - Efficiently mining high average utility itemsets with a tree structure

AU - Lin, Chun Wei

AU - Hong, Tzung Pei

AU - Lu, Wen-Hsiang

PY - 2010/9/29

Y1 - 2010/9/29

N2 - The average utility measure has recently been proposed to reveal a better utility effect of combining several items than the original utility measure. It is defined as the total utility of an itemset divided by its number of items within it. In this paper, a new mining approach with the aid of a tree structure is proposed to efficiently implement the concept. The high average utility pattern tree (HAUP tree) structure is first designed to help keep some related information and then the HAUP-growth algorithm is proposed to mine high average utility itemsets from the tree structure. Experimental results also show that the proposed approach has a better performance than the Apriori-like average utility mining.

AB - The average utility measure has recently been proposed to reveal a better utility effect of combining several items than the original utility measure. It is defined as the total utility of an itemset divided by its number of items within it. In this paper, a new mining approach with the aid of a tree structure is proposed to efficiently implement the concept. The high average utility pattern tree (HAUP tree) structure is first designed to help keep some related information and then the HAUP-growth algorithm is proposed to mine high average utility itemsets from the tree structure. Experimental results also show that the proposed approach has a better performance than the Apriori-like average utility mining.

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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Lin CW, Hong TP, Lu WH. Efficiently mining high average utility itemsets with a tree structure. 於 Intelligent Information and Database Systems - Second International Conference, ACIIDS, Proceedings. PART 1 編輯 2010. p. 131-139. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1). doi: 10.1007/978-3-642-12145-6_14

Efficiently mining high average utility itemsets with a tree structure

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