Mining association rules for label ranking

Cláudio Rebelo De Sá; Carlos Soares; Alípio Mário Jorge; Paulo Azevedo; Joaquim Costa

doi:10.1007/978-3-642-20847-8-36

Mining association rules for label ranking

Cláudio Rebelo De Sá, Carlos Soares, Alípio Mário Jorge, Paulo Azevedo, Joaquim Costa

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

24 Citations (Scopus)

Abstract

Recently, a number of learning algorithms have been adapted for label ranking, including instance-based and tree-based methods. In this paper, we propose an adaptation of association rules for label ranking. The adaptation, which is illustrated in this work with APRIORI Algorithm, essentially consists of using variations of the support and confidence measures based on ranking similarity functions that are suitable for label ranking. We also adapt the method to make a prediction from the possibly conflicting consequents of the rules that apply to an example. Despite having made our adaptation from a very simple variant of association rules for classification, the results clearly show that the method is making valid predictions. Additionally, they show that it competes well with state-of-the-art label ranking algorithms.

Original language	English
Title of host publication	Advances in Knowledge Discovery and Data Mining
Subtitle of host publication	15th Pacific-Asia Conference, PAKDD 2011, Shenzhen, China, May 24-27, 2011, Proceedings
Editors	Joshua Zhexue Huang, Longbing Cao, Jaideep Srivastava
Place of Publication	Berlin, Heidelberg
Publisher	Springer
Pages	432-443
Number of pages	12
ISBN (Electronic)	978-3-642-20847-8
ISBN (Print)	978-3-642-20846-1
DOIs	https://doi.org/10.1007/978-3-642-20847-8-36
Publication status	Published - 8 Jun 2011
Event	15th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2011 - Shenzhen, China, Shenzhen, China Duration: 24 May 2011 → 27 May 2011 Conference number: 15

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer
Volume	6635
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Name	Lecture Notes in Artificial Intelligence
Publisher	Springer

Conference

Conference	15th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2011
Abbreviated title	PAKDD 2011
Country	China
City	Shenzhen
Period	24/05/11 → 27/05/11
Other	24-27 May 2011

Access to Document

10.1007/978-3-642-20847-8-36

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De Sá, C. R., Soares, C., Jorge, A. M., Azevedo, P., & Costa, J. (2011). Mining association rules for label ranking. In J. Z. Huang, L. Cao, & J. Srivastava (Eds.), Advances in Knowledge Discovery and Data Mining: 15th Pacific-Asia Conference, PAKDD 2011, Shenzhen, China, May 24-27, 2011, Proceedings (pp. 432-443). (Lecture Notes in Computer Science; Vol. 6635), (Lecture Notes in Artificial Intelligence). Springer. https://doi.org/10.1007/978-3-642-20847-8-36

De Sá, Cláudio Rebelo ; Soares, Carlos ; Jorge, Alípio Mário ; Azevedo, Paulo ; Costa, Joaquim. / Mining association rules for label ranking. Advances in Knowledge Discovery and Data Mining: 15th Pacific-Asia Conference, PAKDD 2011, Shenzhen, China, May 24-27, 2011, Proceedings. editor / Joshua Zhexue Huang ; Longbing Cao ; Jaideep Srivastava. Berlin, Heidelberg : Springer, 2011. pp. 432-443 (Lecture Notes in Computer Science). (Lecture Notes in Artificial Intelligence).

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title = "Mining association rules for label ranking",

abstract = "Recently, a number of learning algorithms have been adapted for label ranking, including instance-based and tree-based methods. In this paper, we propose an adaptation of association rules for label ranking. The adaptation, which is illustrated in this work with APRIORI Algorithm, essentially consists of using variations of the support and confidence measures based on ranking similarity functions that are suitable for label ranking. We also adapt the method to make a prediction from the possibly conflicting consequents of the rules that apply to an example. Despite having made our adaptation from a very simple variant of association rules for classification, the results clearly show that the method is making valid predictions. Additionally, they show that it competes well with state-of-the-art label ranking algorithms.",

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De Sá, CR, Soares, C, Jorge, AM, Azevedo, P & Costa, J 2011, Mining association rules for label ranking. in JZ Huang, L Cao & J Srivastava (eds), Advances in Knowledge Discovery and Data Mining: 15th Pacific-Asia Conference, PAKDD 2011, Shenzhen, China, May 24-27, 2011, Proceedings. Lecture Notes in Computer Science, vol. 6635, Lecture Notes in Artificial Intelligence, Springer, Berlin, Heidelberg, pp. 432-443, 15th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2011, Shenzhen, China, 24/05/11. https://doi.org/10.1007/978-3-642-20847-8-36

Mining association rules for label ranking. / De Sá, Cláudio Rebelo; Soares, Carlos; Jorge, Alípio Mário; Azevedo, Paulo; Costa, Joaquim.

Advances in Knowledge Discovery and Data Mining: 15th Pacific-Asia Conference, PAKDD 2011, Shenzhen, China, May 24-27, 2011, Proceedings. ed. / Joshua Zhexue Huang; Longbing Cao; Jaideep Srivastava. Berlin, Heidelberg : Springer, 2011. p. 432-443 (Lecture Notes in Computer Science; Vol. 6635), (Lecture Notes in Artificial Intelligence).

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

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AB - Recently, a number of learning algorithms have been adapted for label ranking, including instance-based and tree-based methods. In this paper, we propose an adaptation of association rules for label ranking. The adaptation, which is illustrated in this work with APRIORI Algorithm, essentially consists of using variations of the support and confidence measures based on ranking similarity functions that are suitable for label ranking. We also adapt the method to make a prediction from the possibly conflicting consequents of the rules that apply to an example. Despite having made our adaptation from a very simple variant of association rules for classification, the results clearly show that the method is making valid predictions. Additionally, they show that it competes well with state-of-the-art label ranking algorithms.

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De Sá CR, Soares C, Jorge AM, Azevedo P, Costa J. Mining association rules for label ranking. In Huang JZ, Cao L, Srivastava J, editors, Advances in Knowledge Discovery and Data Mining: 15th Pacific-Asia Conference, PAKDD 2011, Shenzhen, China, May 24-27, 2011, Proceedings. Berlin, Heidelberg: Springer. 2011. p. 432-443. (Lecture Notes in Computer Science). (Lecture Notes in Artificial Intelligence). https://doi.org/10.1007/978-3-642-20847-8-36