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Manage. Inf. Syst."],"published-print":{"date-parts":[[2022,12,31]]},"abstract":"<jats:p>Nowadays, the environments of smart systems for Industry 4.0 and Internet of Things are experiencing fast industrial upgrading. Big data technologies such as design making, event detection, and classification are developed to help manufacturing organizations to achieve smart systems. By applying data analysis, the potential values of rich data can be maximized, which will help manufacturing organizations to finish another round of upgrading. In this article, we propose two new algorithms with respect to big data analysis, namely UFC<jats:italic><jats:sub>gen<\/jats:sub><\/jats:italic>and UFC<jats:italic><jats:sub>fast<\/jats:sub><\/jats:italic>. Both algorithms are designed to collect three types of patterns to help people determine the market positions for different product combinations. We compare these algorithms on various types of datasets, both real and synthetic. The experimental results show that both algorithms can successfully achieve pattern classification by utilizing three different types of interesting patterns from all candidate patterns based on user-specified thresholds of utility and frequency. Furthermore, the list-based UFC<jats:italic><jats:sub>fast<\/jats:sub><\/jats:italic>algorithm outperforms the levelwise-based UFC<jats:italic><jats:sub>gen<\/jats:sub><\/jats:italic>algorithm in terms of both execution time and memory consumption.<\/jats:p>","DOI":"10.1145\/3531480","type":"journal-article","created":{"date-parts":[[2022,7,18]],"date-time":"2022-07-18T12:20:11Z","timestamp":1658146811000},"page":"1-24","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Smart System: Joint Utility and Frequency for Pattern Classification"],"prefix":"10.1145","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5486-1306","authenticated-orcid":false,"given":"Qi","family":"Lin","sequence":"first","affiliation":[{"name":"Jinan University, Guangzhou City, Guangdong Province, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5781-8116","authenticated-orcid":false,"given":"Wensheng","family":"Gan","sequence":"additional","affiliation":[{"name":"Jinan University, Guangzhou City, Guangdong Province, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0850-724X","authenticated-orcid":false,"given":"Yongdong","family":"Wu","sequence":"additional","affiliation":[{"name":"Jinan University, Guangzhou City, Guangdong Province, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7128-9778","authenticated-orcid":false,"given":"Jiahui","family":"Chen","sequence":"additional","affiliation":[{"name":"Guangdong University of Technology, Guangzhou City, Guangdong Province, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6502-472X","authenticated-orcid":false,"given":"Chien-Ming","family":"Chen","sequence":"additional","affiliation":[{"name":"Shandong University of Science and Technology, Qingdao City, Shandong Province, China"}]}],"member":"320","published-online":{"date-parts":[[2023,1,17]]},"reference":[{"issue":"13","key":"e_1_3_1_2_2","doi-asserted-by":"crossref","first-page":"5948","DOI":"10.1016\/j.eswa.2014.03.019","article-title":"Phishing detection based associative classification data mining","volume":"41","author":"Abdelhamid Neda","year":"2014","unstructured":"Neda Abdelhamid, Aladdin Ayesh, and Fadi Thabtah. 2014. 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