Mining both positive and negative association rules from frequent and infrequent itemsets

Xiangjun Dong; Zhendong Niu; Xuelin Shi; Xiaodan Zhang; Donghua Zhu

doi:10.1007/978-3-540-73871-8_13

Mining both positive and negative association rules from frequent and infrequent itemsets

Xiangjun Dong^*
, Zhendong Niu
, Xuelin Shi
, Xiaodan Zhang
, Donghua Zhu

^*Corresponding author for this work

School of Computer Science and Technology

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

41 Citations (Scopus)

Abstract

A lot of new problems may occur when we simultaneously study positive and negative association rules (PNARs), i.e., the forms A⇒B, A⇒¬, ¬A⇒B and ¬A⇒¬B. These problems include how to discover infrequent itemsets, how to generate PNARs correctly, how to solve the problem caused by a single minimum support and so on. Infrequent itemsets become very important because there are many valued negative association rules (NARs) in them. In our previous work, a MLMS model was proposed to discover simultaneously both frequent and infrequent itemsets by using multiple level minimum supports (MLMS) model. In this paper, a new measure VARCC which combines correlation coefficient and minimum confidence is proposed and a corresponding algorithm PNAR_MLMS is also proposed to generate PNARs correctly from the frequent and infrequent itemsets discovered by the MLMS model. The experimental results show that the measure and the algorithm are effective.

Original language	English
Title of host publication	Advanced Data Mining and Applications - Third International Conference, ADMA 2007, Proceedings
Publisher	Springer Verlag
Pages	122-133
Number of pages	12
ISBN (Print)	9783540738701
DOIs	https://doi.org/10.1007/978-3-540-73871-8_13
Publication status	Published - 2007
Event	3rd International Conference on Advanced Data Mining and Applications, ADMA 2007 - Harbin, China Duration: 6 Aug 2007 → 8 Aug 2007

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	4632 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	3rd International Conference on Advanced Data Mining and Applications, ADMA 2007
Country/Territory	China
City	Harbin
Period	6/08/07 → 8/08/07

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10.1007/978-3-540-73871-8_13

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Dong, X., Niu, Z., Shi, X., Zhang, X., & Zhu, D. (2007). Mining both positive and negative association rules from frequent and infrequent itemsets. In Advanced Data Mining and Applications - Third International Conference, ADMA 2007, Proceedings (pp. 122-133). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4632 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-540-73871-8_13

Dong, Xiangjun ; Niu, Zhendong ; Shi, Xuelin et al. / Mining both positive and negative association rules from frequent and infrequent itemsets. Advanced Data Mining and Applications - Third International Conference, ADMA 2007, Proceedings. Springer Verlag, 2007. pp. 122-133 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Mining both positive and negative association rules from frequent and infrequent itemsets",

abstract = "A lot of new problems may occur when we simultaneously study positive and negative association rules (PNARs), i.e., the forms A⇒B, A⇒¬, ¬A⇒B and ¬A⇒¬B. These problems include how to discover infrequent itemsets, how to generate PNARs correctly, how to solve the problem caused by a single minimum support and so on. Infrequent itemsets become very important because there are many valued negative association rules (NARs) in them. In our previous work, a MLMS model was proposed to discover simultaneously both frequent and infrequent itemsets by using multiple level minimum supports (MLMS) model. In this paper, a new measure VARCC which combines correlation coefficient and minimum confidence is proposed and a corresponding algorithm PNAR\_MLMS is also proposed to generate PNARs correctly from the frequent and infrequent itemsets discovered by the MLMS model. The experimental results show that the measure and the algorithm are effective.",

author = "Xiangjun Dong and Zhendong Niu and Xuelin Shi and Xiaodan Zhang and Donghua Zhu",

year = "2007",

doi = "10.1007/978-3-540-73871-8\_13",

language = "English",

isbn = "9783540738701",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "122--133",

booktitle = "Advanced Data Mining and Applications - Third International Conference, ADMA 2007, Proceedings",

address = "Germany",

note = "3rd International Conference on Advanced Data Mining and Applications, ADMA 2007 ; Conference date: 06-08-2007 Through 08-08-2007",

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Dong, X, Niu, Z, Shi, X, Zhang, X & Zhu, D 2007, Mining both positive and negative association rules from frequent and infrequent itemsets. in Advanced Data Mining and Applications - Third International Conference, ADMA 2007, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4632 LNAI, Springer Verlag, pp. 122-133, 3rd International Conference on Advanced Data Mining and Applications, ADMA 2007, Harbin, China, 6/08/07. https://doi.org/10.1007/978-3-540-73871-8_13

Mining both positive and negative association rules from frequent and infrequent itemsets. / Dong, Xiangjun; Niu, Zhendong; Shi, Xuelin et al.
Advanced Data Mining and Applications - Third International Conference, ADMA 2007, Proceedings. Springer Verlag, 2007. p. 122-133 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4632 LNAI).

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

TY - GEN

T1 - Mining both positive and negative association rules from frequent and infrequent itemsets

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AU - Niu, Zhendong

AU - Shi, Xuelin

AU - Zhang, Xiaodan

AU - Zhu, Donghua

PY - 2007

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N2 - A lot of new problems may occur when we simultaneously study positive and negative association rules (PNARs), i.e., the forms A⇒B, A⇒¬, ¬A⇒B and ¬A⇒¬B. These problems include how to discover infrequent itemsets, how to generate PNARs correctly, how to solve the problem caused by a single minimum support and so on. Infrequent itemsets become very important because there are many valued negative association rules (NARs) in them. In our previous work, a MLMS model was proposed to discover simultaneously both frequent and infrequent itemsets by using multiple level minimum supports (MLMS) model. In this paper, a new measure VARCC which combines correlation coefficient and minimum confidence is proposed and a corresponding algorithm PNAR_MLMS is also proposed to generate PNARs correctly from the frequent and infrequent itemsets discovered by the MLMS model. The experimental results show that the measure and the algorithm are effective.

AB - A lot of new problems may occur when we simultaneously study positive and negative association rules (PNARs), i.e., the forms A⇒B, A⇒¬, ¬A⇒B and ¬A⇒¬B. These problems include how to discover infrequent itemsets, how to generate PNARs correctly, how to solve the problem caused by a single minimum support and so on. Infrequent itemsets become very important because there are many valued negative association rules (NARs) in them. In our previous work, a MLMS model was proposed to discover simultaneously both frequent and infrequent itemsets by using multiple level minimum supports (MLMS) model. In this paper, a new measure VARCC which combines correlation coefficient and minimum confidence is proposed and a corresponding algorithm PNAR_MLMS is also proposed to generate PNARs correctly from the frequent and infrequent itemsets discovered by the MLMS model. The experimental results show that the measure and the algorithm are effective.

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SN - 9783540738701

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

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BT - Advanced Data Mining and Applications - Third International Conference, ADMA 2007, Proceedings

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ER -

Dong X, Niu Z, Shi X, Zhang X, Zhu D. Mining both positive and negative association rules from frequent and infrequent itemsets. In Advanced Data Mining and Applications - Third International Conference, ADMA 2007, Proceedings. Springer Verlag. 2007. p. 122-133. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-540-73871-8_13

Mining both positive and negative association rules from frequent and infrequent itemsets

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