Blending Association Rules for Knowledge Discovery in Big Data

Anaissi, A; Goyal, M

Blending Association Rules for Knowledge Discovery in Big Data

Anaissi, A Goyal, M

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Publisher:: IGI Global
Publication Type:: Chapter
Citation:: Enabling Technologies and Architectures for Next-Generation Networking Capabilities, 2019, pp. 254 - 271 (18)
Issue Date:: 2019-01-17

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Field	Value	Language
dc.contributor.author	Anaissi, A	en_US
dc.contributor.author	Goyal, M https://orcid.org/0000-0003-2853-9393	en_US
dc.date.issued	2019-01-17	en_US
dc.identifier.citation	Enabling Technologies and Architectures for Next-Generation Networking Capabilities, 2019, pp. 254 - 271 (18)	en_US
dc.identifier.isbn	9781522560234	en_US
dc.identifier.uri	http://hdl.handle.net/10453/134945
dc.description.abstract	Data mining techniques have been widely applied in several domains to support a variety of business-related applications such as market basket analysis. For instance, basket market transaction accumulate large amounts of customer purchase data from their day-to-day operations. This paper delivers a strategy for the implementation of a systematic analysis framework built on the established principles used in data mining and machine learning areas.We employ Apriori and FP-growth algorithms coupled with support vector machine to implement our recommendation systems. Experiments are done using a real market dataset and the 0.632+ bootstrap method is used here in order to evaluate our framework. The obtained results suggest that the proposed framework will be able to generate benefits for grocery chain using a real-world grocery store data. FP-growth algorithm shows better performance over Apriori in terms of time complexity.	en_US
dc.publisher	IGI Global	en_US
dc.relation.ispartof	Enabling Technologies and Architectures for Next-Generation Networking Capabilities	en_US
dc.relation.isbasedon	10.4018/978-1-5225-6023-4.ch012	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Blending Association Rules for Knowledge Discovery in Big Data	en_US
dc.type	Chapter
utslib.location	USA	en_US
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/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Software
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access	*
pubs.consider-herdc	true	en_US
pubs.place-of-publication	USA	en_US
pubs.publication-status	Published online	en_US

Abstract:

Data mining techniques have been widely applied in several domains to support a variety of business-related applications such as market basket analysis. For instance, basket market transaction accumulate large amounts of customer purchase data from their day-to-day operations. This paper delivers a strategy for the implementation of a systematic analysis framework built on the established principles used in data mining and machine learning areas.We employ Apriori and FP-growth algorithms coupled with support vector machine to implement our recommendation systems. Experiments are done using a real market dataset and the 0.632+ bootstrap method is used here in order to evaluate our framework. The obtained results suggest that the proposed framework will be able to generate benefits for grocery chain using a real-world grocery store data. FP-growth algorithm shows better performance over Apriori in terms of time complexity.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/134945