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Internet Technol."],"published-print":{"date-parts":[[2021,2,28]]},"abstract":"<jats:p>\n            Consumer behavior plays a very important role in economics and targeted marketing. However, understanding economic consumer behavior is quite challenging, such as finding credible and reliable information on product profitability. Different from frequent pattern mining, utility-oriented mining integrates utility theory and data mining. Utility mining is a useful tool for understanding economic consumer behavior. Traditional algorithms for mining high-utility patterns (HUPs) applies a single\/uniform minimum utility threshold (\n            <jats:italic>minutil<\/jats:italic>\n            ) to obtain the set of HUPs, but in some real-life circumstances, some specific products may bring lower utilities compared with others, but their profit may offer some vital information. If\n            <jats:italic>minutil<\/jats:italic>\n            is set high, the patterns with low\n            <jats:italic>minutil<\/jats:italic>\n            are missed; if\n            <jats:italic>minutil<\/jats:italic>\n            is set low, the number of patterns becomes unmanageable. In this article, an efficient one-phase utility-oriented pattern mining algorithm, called HIMU, is proposed for mining HUPs with varied item-specific minimum utility. A novel tree structure called a multiple item utility set-enumeration tree (MIU-tree) and the global sorted and the conditional downward closure properties are introduced in HIMU. In addition, we extended the compact utility-list structure to keep the necessary information, and thus this one-phase HIMU model greatly reduces the computational costs and memory requirements. Moreover, two pruning strategies are then extended to enhance the performance. We conducted extensive experiments in several synthetic and real-world datasets; the results indicate that the designed one-phase HIMU algorithm can address the \u201c\n            <jats:italic>rare item problem<\/jats:italic>\n            \u201d and has better performance than the state-of-the-art algorithms in terms of runtime, memory usage, and scalability. Furthermore, the enhanced algorithms outperform the non-optimized HIMU approach.\n          <\/jats:p>","DOI":"10.1145\/3425498","type":"journal-article","created":{"date-parts":[[2021,1,5]],"date-time":"2021-01-05T18:32:48Z","timestamp":1609871568000},"page":"1-32","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["Beyond Frequency"],"prefix":"10.1145","volume":"21","author":[{"given":"Wensheng","family":"Gan","sequence":"first","affiliation":[{"name":"Jinan University, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8768-9709","authenticated-orcid":false,"given":"Jerry Chun-Wei","family":"Lin","sequence":"additional","affiliation":[{"name":"Western Norway University of Applied Sciences, Norway"}]},{"given":"Philippe","family":"Fournier-Viger","sequence":"additional","affiliation":[{"name":"Harbin Institute of Technology (Shenzhen), China"}]},{"given":"Han-Chieh","family":"Chao","sequence":"additional","affiliation":[{"name":"National Dong Hwa University, Taiwan"}]},{"given":"Philip S.","family":"Yu","sequence":"additional","affiliation":[{"name":"University of Illinois at Chicago, USA"}]}],"member":"320","published-online":{"date-parts":[[2021,1,5]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/170035.170072"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.5555\/645480.655281"},{"key":"e_1_2_1_3_1","volume-title":"Proceedings of the 20th International Conference on Very Large Data Bases","volume":"1215","author":"Agrawal Rakesh","year":"1994","unstructured":"Rakesh Agrawal , Ramakrishnan Srikant , et\u00a0al. 1994 . Fast algorithms for mining association rules . In Proceedings of the 20th International Conference on Very Large Data Bases , Vol. 1215 . 487--499. Rakesh Agrawal, Ramakrishnan Srikant, et\u00a0al. 1994. Fast algorithms for mining association rules. In Proceedings of the 20th International Conference on Very Large Data Bases, Vol. 1215. 487--499."},{"key":"e_1_2_1_4_1","volume-title":"Quest synthetic data generator","author":"Agrawal Rakesh","year":"1994","unstructured":"Rakesh Agrawal and Ramakrishnan. Quest synthetic data generator . 1994 . Retrieved from http:\/\/www.Almaden.ibm.com\/cs\/quest\/syndata.html. Rakesh Agrawal and Ramakrishnan. Quest synthetic data generator. 1994. 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Retrieved from http:\/\/msdn.microsoft.com\/en-us\/library\/aa217032(SQL.80).aspx."},{"key":"e_1_2_1_10_1","volume-title":"Tseng","author":"Fournier-Viger Philippe","year":"2014","unstructured":"Philippe Fournier-Viger , Cheng-Wei Wu , Souleymane Zida , and Vincent S . Tseng . 2014 . FHM : Faster high-utility itemset mining using estimated utility co-occurrence pruning. In Proceedings of the International Symposium on Methodologies for Intelligent Systems. Springer , 83--92. Philippe Fournier-Viger, Cheng-Wei Wu, Souleymane Zida, and Vincent S. Tseng. 2014. FHM: Faster high-utility itemset mining using estimated utility co-occurrence pruning. In Proceedings of the International Symposium on Methodologies for Intelligent Systems. Springer, 83--92."},{"volume-title":"Proceedings of the IEEE International Conference on Big Data. 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