Actionable combined high utility itemset mining

Shao, J; Yin, J; Liu, W; Cao, L

Actionable combined high utility itemset mining

Shao, J Yin, J Liu, W

Cao, L

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the National Conference on Artificial Intelligence, 2015, 6 pp. 4206 - 4207
Issue Date:: 2015-06-01

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Field	Value	Language
dc.contributor.author	Shao, J	en_US
dc.contributor.author	Yin, J	en_US
dc.contributor.author	Liu, W https://orcid.org/0000-0002-3003-1313	en_US
dc.contributor.author	Cao, L https://orcid.org/0000-0003-1562-9429	en_US
dc.date.issued	2015-06-01	en_US
dc.identifier.citation	Proceedings of the National Conference on Artificial Intelligence, 2015, 6 pp. 4206 - 4207	en_US
dc.identifier.isbn	9781577357049	en_US
dc.identifier.uri	http://hdl.handle.net/10453/123087
dc.description.abstract	© Copyright 2015, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. The itemsets discovered by traditional High Utility Itemsets Mining (HUIM) methods are more useful than frequent itemset mining outcomes; however, they are usually disordered and not actionable, and sometime accidental, because the utility is the only judgement and no relations among itemsets are considered. In this paper, we introduce the concept of combined mining to select combined itemsets that are not only high utility and high frequency, but also involving relations between itemsets. An effective method for mining such actionable combined high utility itemsets is proposed. The experimental results are promising, compared to those from traditional HUIM algorithm (UP-Growth).	en_US
dc.relation.ispartof	Proceedings of the National Conference on Artificial Intelligence	en_US
dc.title	Actionable combined high utility itemset mining	en_US
dc.type	Conference Proceeding
utslib.citation.volume	6	en_US
utslib.for	080101 Adaptive Agents and Intelligent Robotics	en_US
utslib.for	080110 Simulation and Modelling	en_US
utslib.for	080109 Pattern Recognition and Data Mining	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	6	en_US

Abstract:

© Copyright 2015, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. The itemsets discovered by traditional High Utility Itemsets Mining (HUIM) methods are more useful than frequent itemset mining outcomes; however, they are usually disordered and not actionable, and sometime accidental, because the utility is the only judgement and no relations among itemsets are considered. In this paper, we introduce the concept of combined mining to select combined itemsets that are not only high utility and high frequency, but also involving relations between itemsets. An effective method for mining such actionable combined high utility itemsets is proposed. The experimental results are promising, compared to those from traditional HUIM algorithm (UP-Growth).

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http://hdl.handle.net/10453/123087