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Knowl. Discov. Data"],"published-print":{"date-parts":[[2022,6,30]]},"abstract":"<jats:p>\n            Nonoverlapping sequential pattern mining is an important type of sequential pattern mining (SPM) with gap constraints, which not only can reveal interesting patterns to users but also can effectively reduce the search space using the Apriori (anti-monotonicity) property. However, the existing algorithms do not focus on attributes of interest to users, meaning that existing methods may discover many frequent patterns that are redundant. To solve this problem, this article proposes a task called nonoverlapping three-way sequential pattern (NTP) mining, where attributes are categorized according to three levels of interest: strong, medium, and weak interest. NTP mining can effectively avoid mining redundant patterns since the NTPs are composed of strong and medium interest items. Moreover, NTPs can avoid serious deviations (the occurrence is significantly different from its pattern) since gap constraints cannot match with strong interest patterns. To mine NTPs, an effective algorithm is put forward, called NTP-Miner, which applies two main steps: support (frequency occurrence) calculation and candidate pattern generation. To calculate the support of an NTP, depth-first and backtracking strategies are adopted, which do not require creating a whole Nettree structure, meaning that many redundant nodes and parent\u2013child relationships do not need to be created. Hence, time and space efficiency is improved. To generate candidate patterns while reducing their number, NTP-Miner employs a pattern join strategy and only mines patterns of strong and medium interest. Experimental results on stock market and protein datasets show that NTP-Miner not only is more efficient than other competitive approaches but can also help users find more valuable patterns. More importantly, NTP mining has achieved better performance than other competitive methods in clustering tasks. Algorithms and data are available at:\n            <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/github.com\/wuc567\/Pattern-Mining\/tree\/master\/NTP-Miner\">https:\/\/github.com\/wuc567\/Pattern-Mining\/tree\/master\/NTP-Miner<\/jats:ext-link>\n            .\n          <\/jats:p>","DOI":"10.1145\/3480245","type":"journal-article","created":{"date-parts":[[2021,10,23]],"date-time":"2021-10-23T04:28:40Z","timestamp":1634963320000},"page":"1-21","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":32,"title":["NTP-Miner: Nonoverlapping Three-Way Sequential Pattern Mining"],"prefix":"10.1145","volume":"16","author":[{"given":"Youxi","family":"Wu","sequence":"first","affiliation":[{"name":"School of Artificial Intelligence, Hebei University of Technology and Hebei Key Laboratory of Big Data Computing, Tianjin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lanfang","family":"Luo","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Hebei University of Technology, Tianjin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yan","family":"Li","sequence":"additional","affiliation":[{"name":"School of Economics and Management, Hebei University of Technology, Tianjin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lei","family":"Guo","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Philippe","family":"Fournier-Viger","sequence":"additional","affiliation":[{"name":"School of Humanities and Social Sciences, Harbin Institute of Technology (Shenzhen), Shenzhen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xingquan","family":"Zhu","sequence":"additional","affiliation":[{"name":"Department of Computer &amp; Electrical Engineering and Computer Science, Florida Atlantic University, FL"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xindong","family":"Wu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology), Ministry of Education and Mininglamp Academy of Sciences, Mininglamp Technology, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,10,22]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2019.2896267"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1145\/3314107"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1145\/3399671"},{"key":"e_1_3_2_5_2","doi-asserted-by":"crossref","unstructured":"Philippe Fournier-Viger J. 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