CHUQI-Miner: Mining Correlated Quantitative High Utility Itemsets

Mourad Nouioua; Philippe Fournier-Viger; Jun Feng Qu; Jerry Chun Wei Lin; Wensheng Gan; Wei Song

doi:10.1109/ICDMW53433.2021.00079

CHUQI-Miner: Mining Correlated Quantitative High Utility Itemsets

Mourad Nouioua, Philippe Fournier-Viger^*, Jun Feng Qu, Jerry Chun Wei Lin, Wensheng Gan, Wei Song

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Scopus citations

Abstract

To discover patterns of high importance in data, a popular data science task is high utility itemset mining (HUIM). It aims at discovering all sets of values that have a high utility (importance) in database records. A key application is to find products purchased together in online stores that yield a high profit (utility), as it can provide insights for marketing and product recommendation. But HUIM has two key limitations. First, the discovered patterns do not provide information about the quantities of items. But in real-life, quantities are important (e.g. buying 1 bread is not the same as buying 12 breads). Second, it is observed in real shopping data that many itemsets yield a high utility (profit) but contain weakly correlated items. Such itemsets can be misleading as their joint sale may just appear by chance. This paper addresses both issues by proposing a novel algorithm called CHUQI-Miner (Correlated High Utility Quantitative Itemset-Miner). It extends the state-of-the-art HUQI-Miner algorithm for quantitative high utility itemset mining with the bond correlation measure. This allows finding strongly correlated high utility itemsets with quantities. Experiments on retail data show that the algorithm is efficient and can filter a huge amount of spurious itemsets.

Original language	English
Title of host publication	Proceedings - 21st IEEE International Conference on Data Mining Workshops, ICDMW 2021
Editors	Bing Xue, Mykola Pechenizkiy, Yun Sing Koh
Publisher	IEEE Computer Society
Pages	599-606
Number of pages	8
ISBN (Electronic)	9781665424271
DOIs	https://doi.org/10.1109/ICDMW53433.2021.00079
State	Published - 2021
Externally published	Yes
Event	21st IEEE International Conference on Data Mining Workshops, ICDMW 2021 - Virtual, Online, New Zealand Duration: 7 Dec 2021 → 10 Dec 2021

Publication series

Name	IEEE International Conference on Data Mining Workshops, ICDMW
Volume	2021-December
ISSN (Print)	2375-9232
ISSN (Electronic)	2375-9259

Conference

Conference	21st IEEE International Conference on Data Mining Workshops, ICDMW 2021
Country/Territory	New Zealand
City	Virtual, Online
Period	7/12/21 → 10/12/21

Bibliographical note

Publisher Copyright:
© 2021 IEEE.

Keywords

bond measure
correlation
high utility itemsets
quantitative itemsets

ASJC Scopus subject areas

Computer Science Applications
Software

Access to Document

10.1109/ICDMW53433.2021.00079

Cite this

Nouioua, M., Fournier-Viger, P., Qu, J. F., Lin, J. C. W., Gan, W., & Song, W. (2021). CHUQI-Miner: Mining Correlated Quantitative High Utility Itemsets. In B. Xue, M. Pechenizkiy, & Y. S. Koh (Eds.), Proceedings - 21st IEEE International Conference on Data Mining Workshops, ICDMW 2021 (pp. 599-606). (IEEE International Conference on Data Mining Workshops, ICDMW; Vol. 2021-December). IEEE Computer Society. https://doi.org/10.1109/ICDMW53433.2021.00079

Nouioua, Mourad ; Fournier-Viger, Philippe ; Qu, Jun Feng et al. / CHUQI-Miner : Mining Correlated Quantitative High Utility Itemsets. Proceedings - 21st IEEE International Conference on Data Mining Workshops, ICDMW 2021. editor / Bing Xue ; Mykola Pechenizkiy ; Yun Sing Koh. IEEE Computer Society, 2021. pp. 599-606 (IEEE International Conference on Data Mining Workshops, ICDMW).

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abstract = "To discover patterns of high importance in data, a popular data science task is high utility itemset mining (HUIM). It aims at discovering all sets of values that have a high utility (importance) in database records. A key application is to find products purchased together in online stores that yield a high profit (utility), as it can provide insights for marketing and product recommendation. But HUIM has two key limitations. First, the discovered patterns do not provide information about the quantities of items. But in real-life, quantities are important (e.g. buying 1 bread is not the same as buying 12 breads). Second, it is observed in real shopping data that many itemsets yield a high utility (profit) but contain weakly correlated items. Such itemsets can be misleading as their joint sale may just appear by chance. This paper addresses both issues by proposing a novel algorithm called CHUQI-Miner (Correlated High Utility Quantitative Itemset-Miner). It extends the state-of-the-art HUQI-Miner algorithm for quantitative high utility itemset mining with the bond correlation measure. This allows finding strongly correlated high utility itemsets with quantities. Experiments on retail data show that the algorithm is efficient and can filter a huge amount of spurious itemsets.",

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author = "Mourad Nouioua and Philippe Fournier-Viger and Qu, \{Jun Feng\} and Lin, \{Jerry Chun Wei\} and Wensheng Gan and Wei Song",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 21st IEEE International Conference on Data Mining Workshops, ICDMW 2021 ; Conference date: 07-12-2021 Through 10-12-2021",

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doi = "10.1109/ICDMW53433.2021.00079",

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Nouioua, M, Fournier-Viger, P, Qu, JF, Lin, JCW, Gan, W & Song, W 2021, CHUQI-Miner: Mining Correlated Quantitative High Utility Itemsets. in B Xue, M Pechenizkiy & YS Koh (eds), Proceedings - 21st IEEE International Conference on Data Mining Workshops, ICDMW 2021. IEEE International Conference on Data Mining Workshops, ICDMW, vol. 2021-December, IEEE Computer Society, pp. 599-606, 21st IEEE International Conference on Data Mining Workshops, ICDMW 2021, Virtual, Online, New Zealand, 7/12/21. https://doi.org/10.1109/ICDMW53433.2021.00079

CHUQI-Miner: Mining Correlated Quantitative High Utility Itemsets. / Nouioua, Mourad; Fournier-Viger, Philippe; Qu, Jun Feng et al.
Proceedings - 21st IEEE International Conference on Data Mining Workshops, ICDMW 2021. ed. / Bing Xue; Mykola Pechenizkiy; Yun Sing Koh. IEEE Computer Society, 2021. p. 599-606 (IEEE International Conference on Data Mining Workshops, ICDMW; Vol. 2021-December).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Nouioua, Mourad

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AU - Lin, Jerry Chun Wei

AU - Gan, Wensheng

AU - Song, Wei

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Nouioua M, Fournier-Viger P, Qu JF, Lin JCW, Gan W, Song W. CHUQI-Miner: Mining Correlated Quantitative High Utility Itemsets. In Xue B, Pechenizkiy M, Koh YS, editors, Proceedings - 21st IEEE International Conference on Data Mining Workshops, ICDMW 2021. IEEE Computer Society. 2021. p. 599-606. (IEEE International Conference on Data Mining Workshops, ICDMW). doi: 10.1109/ICDMW53433.2021.00079

CHUQI-Miner: Mining Correlated Quantitative High Utility Itemsets

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this