HIGH UTILITY ITEMSETS MINING

Ying Liu (), Jianwei Li (), Wei-Keng Liao (), Alok Choudhary () and Yong Shi ()
Additional contact information
Ying Liu: School of Information Science and Engineering, Graduate University of Chinese Academy of Sciences, Research Center on Fictitious Economy and Data Science, Chinese Academy of Sciences, 80 ZhongGuanCun East Road, Beijing 100190, China
Jianwei Li: Bloomberg L.P., USA;
Wei-Keng Liao: Department of Electrical Engineering and Computer Science, Northwestern University, Evanston, IL 60208, USA
Alok Choudhary: Department of Electrical Engineering and Computer Science, Northwestern University, Evanston, IL 60208, USA
Yong Shi: Research Center on Fictitious Economy and Data Science, Chinese Academy of Sciences, Haidian District, Beijing 100190, China;

International Journal of Information Technology & Decision Making (IJITDM), 2010, vol. 09, issue 06, 905-934

Abstract: High utility itemsets mining identifies itemsets whose utility satisfies a given threshold. It allows users to quantify the usefulness or preferences of items using different values. Thus, it reflects the impact of different items. High utility itemsets mining is useful in decision-making process of many applications, such as retail marketing and Web service, since items are actually different in many aspects in real applications. However, due to the lack of "downward closure property", the cost of candidate generation of high utility itemsets mining is intolerable in terms of time and memory space. This paper presents a Two-Phase algorithm which can efficiently prune down the number of candidates and precisely obtain the complete set of high utility itemsets. The performance of our algorithm is evaluated by applying it to synthetic databases and two real-world applications. It performs very efficiently in terms of speed and memory cost on large databases composed of short transactions, which are difficult for existing high utility itemsets mining algorithms to handle. Experiments on real-world applications demonstrate the significance of high utility itemsets in business decision-making, as well as the difference between frequent itemsets and high utility itemsets.

Keywords: Data mining; utility mining; business intelligence (search for similar items in EconPapers)
Date: 2010
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.worldscientific.com/doi/abs/10.1142/S0219622010004159
Access to full text is restricted to subscribers

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wsi:ijitdm:v:09:y:2010:i:06:n:s0219622010004159

Ordering information: This journal article can be ordered from

DOI: 10.1142/S0219622010004159

Access Statistics for this article

International Journal of Information Technology & Decision Making (IJITDM) is currently edited by Yong Shi

More articles in International Journal of Information Technology & Decision Making (IJITDM) from World Scientific Publishing Co. Pte. Ltd.
Bibliographic data for series maintained by Tai Tone Lim ().