Content Provider	IEEE Xplore Digital Library
Author	Amphawan, K. Surarerks, A.
Copyright Year	2015
Description	Author affiliation: Comput. Innovation Lab., Burapha Univ., Chonburi, Thailand (Amphawan, K.) \|\| Comput. Eng., Chulalongkorn Univ. Bangkok, Bangkok, Thailand (Surarerks, A.)
Abstract	High utility itemsets mining (HUIM) is an interesting topic in data mining which can be applied in a wide range of applications, for example, on retail marketing-finding sets of sold products giving high profit, low cost, etc. However, HUIM only considers utility values of items/itemsets which may be insufficient to observe buying behavior of customers. To address this issue, we here introduce an approach to add regularity constraint into high utility itemsets mining. Based on this approach, sets of cooccurrence items with high utility values and regular occurrence, called high utility-regular itemsets (HURIs), are regarded as interesting itemsets. To mine HURIs, an efficient single-pass algorithm, called HURI-UL, is proposed. HURI-UL applies concept of remaining and overestimated utilities of itemsets to early prune search space (uninteresting itemsets) and also utilizes utility list structure to efficiently maintain utility values and occurrence information of itemsets. Experimental results on real dataseis show that our proposed HURI-UL is efficient to discover high utility itemsets with regular occurrence.
Starting Page	1
Ending Page	6
File Size	819396
Page Count	6
File Format	PDF
ISBN	9781467381420
e-ISBN	9781467381437
DOI	10.1109/ICAICTA.2015.7335348
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-08-19
Publisher Place	Thailand
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	High utility itemsets Regularity constraint Itemsets mining Yttrium Data mining
Content Type	Text
Resource Type	Article

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