Mining actionable combined high utility incremental and associated patterns

Shao, J; Meng, X; Cao, L

Mining actionable combined high utility incremental and associated patterns

Shao, J Meng, X Cao, L

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Publication Type:: Conference Proceeding
Citation:: AUS 2016 - 2016 IEEE/CSAA International Conference on Aircraft Utility Systems, 2016, pp. 1164 - 1169
Issue Date:: 2016-11-17

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Field	Value	Language
dc.contributor.author	Shao, J	en_US
dc.contributor.author	Meng, X	en_US
dc.contributor.author	Cao, L https://orcid.org/0000-0003-1562-9429	en_US
dc.date.issued	2016-11-17	en_US
dc.identifier.citation	AUS 2016 - 2016 IEEE/CSAA International Conference on Aircraft Utility Systems, 2016, pp. 1164 - 1169	en_US
dc.identifier.isbn	9781509010875	en_US
dc.identifier.uri	http://hdl.handle.net/10453/122898
dc.description.abstract	© 2016 IEEE. High Utility Itemsets(HUI) Mining, instead of Frequent Pattern Mining (FIM), has been an attractive theme in data mining domain for over a decade since it can be regarded as an alternative way for researchers to identify actionable patterns. In addition, the necessity of decision-making actions and behavior-oriented strategies based on large amount of informative data impels the significance of discovering actionable patterns to be widely admitted. The current HUIM research focus has been on improving the efficiency to make algorithms faster and more stable. However, the coupling relationships between items in given itemsets are ignored. For example, the utility of one itemset might be lower than the manager expected until one additional item takes part in; and vice versa, the utility of an itemset might drop sharply when another one joins in. What's more, it is not occasional to find out that quite a lot of redundant itemsets sharing the same underlying item are presented based on existing academic HUI mining methods. Store managers would not make expected profits based on such results which makes the results not actionable at all. To this end, here we introduce a new framework for mining actionable patterns, called Mining Utility Associated Patterns (MUAP), which aims to find high utility incremental and strongly associated item/itemset with combined incorporating criteria. The outputs of this algorithm are convincing on real datasets as well as synthetic datasets.	en_US
dc.relation.ispartof	AUS 2016 - 2016 IEEE/CSAA International Conference on Aircraft Utility Systems	en_US
dc.relation.isbasedon	10.1109/AUS.2016.7748234	en_US
dc.title	Mining actionable combined high utility incremental and associated patterns	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	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/Strength - AAI - Advanced Analytics Institute Research Centre
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

© 2016 IEEE. High Utility Itemsets(HUI) Mining, instead of Frequent Pattern Mining (FIM), has been an attractive theme in data mining domain for over a decade since it can be regarded as an alternative way for researchers to identify actionable patterns. In addition, the necessity of decision-making actions and behavior-oriented strategies based on large amount of informative data impels the significance of discovering actionable patterns to be widely admitted. The current HUIM research focus has been on improving the efficiency to make algorithms faster and more stable. However, the coupling relationships between items in given itemsets are ignored. For example, the utility of one itemset might be lower than the manager expected until one additional item takes part in; and vice versa, the utility of an itemset might drop sharply when another one joins in. What's more, it is not occasional to find out that quite a lot of redundant itemsets sharing the same underlying item are presented based on existing academic HUI mining methods. Store managers would not make expected profits based on such results which makes the results not actionable at all. To this end, here we introduce a new framework for mining actionable patterns, called Mining Utility Associated Patterns (MUAP), which aims to find high utility incremental and strongly associated item/itemset with combined incorporating criteria. The outputs of this algorithm are convincing on real datasets as well as synthetic datasets.

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http://hdl.handle.net/10453/122898