{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:24:10Z","timestamp":1777703050013,"version":"3.51.4"},"reference-count":20,"publisher":"SAGE Publications","issue":"5","license":[{"start":{"date-parts":[[2019,5,14]],"date-time":"2019-05-14T00:00:00Z","timestamp":1557792000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"published-print":{"date-parts":[[2019,5,14]]},"abstract":"<jats:p>There are many problems were the objects under study are described by mixed data (numerical and non numerical features) and similarity functions different from the exact matching are usually employed to compare them. Some algorithms for mining frequent patterns allow the use of Boolean similarity functions different from exact matching. However, they do not allow the use of non Boolean similarity functions. Transforming a non Boolean similarity function into a Boolean one, and then applying the previous algorithms for mining frequent patterns, could lead to loss some patterns, and even more to generate some other patterns which indeed should not be considered as frequent similar patterns. In this paper, we extend the similar frequent pattern mining by allowing the use of non Boolean similarity functions. Several properties for pruning the search space of frequent similar patterns and a data structure that allows computing the frequency of patterns candidates, are proposed. Also, three algorithms for mining frequent patterns using non Boolean similarity functions are proposed. Experimental results show the efficiency and efficacy of the algorithms. The proposed algorithms obtain better patterns for classification than those patterns obtained by traditional frequent pattern miners, and miners using Boolean similarity functions.<\/jats:p>","DOI":"10.3233\/jifs-179040","type":"journal-article","created":{"date-parts":[[2019,5,14]],"date-time":"2019-05-14T12:09:41Z","timestamp":1557835781000},"page":"4931-4944","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":2,"title":["Frequent similar pattern mining using non Boolean similarity functions"],"prefix":"10.1177","volume":"36","author":[{"given":"Ansel Y.","family":"Rodr\u00edguez-Gonz\u00e1lez","sequence":"first","affiliation":[{"name":"Mexican National Research Council (CONACyT)"},{"name":"CICESE-UT3, Andador 10 #109, Ciudad del Conocimiento, Tepic, Nayarit, M\u00e9xico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jos\u00e9 F.","family":"Mart\u00ednez-Trinidad","sequence":"additional","affiliation":[{"name":"Department of Computer Sciences, National Institute of Astrophysics, Optics and Electronics (INAOE), Luis Enrique Erro 1, Tonantzintla, Puebla, 72840, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jes\u00fas A.","family":"Carrasco-Ochoa","sequence":"additional","affiliation":[{"name":"Department of Computer Sciences, National Institute of Astrophysics, Optics and Electronics (INAOE), Luis Enrique Erro 1, Tonantzintla, Puebla, 72840, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jos\u00e9","family":"Ruiz-Shulcloper","sequence":"additional","affiliation":[{"name":"University of Informatics Sciences (UCI), Carretera a San Antonio de los Ba\u00f1os, Km. 2 1\/2, Havana, Cuba"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mat\u00edas","family":"Alvarado-Mentado","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Center of Research and Advanced Studies (CINVESTAV), Instituto Polit\u00e9cnico Nacional 2508, D.F., 07300, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2019,5,14]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-006-0059-1"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2014.04.012"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/TAAI.2013.25"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2016.01.002"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2012.08.028"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2015.04.020"},{"key":"e_1_3_2_8_2","first-page":"71","article-title":"Association rules mining: A recent overview","volume":"32","author":"Kotsiantis S.","year":"2006","unstructured":"KotsiantisS. and KanellopoulosD., Association rules mining: A recent overview, International Transactions on Computer Science and Engineering32 (2006), 71\u201382.","journal-title":"International Transactions on Computer Science and Engineering"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2012.03.057"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1145\/775047.775110"},{"key":"e_1_3_2_11_2","first-page":"207","volume-title":"Proceedings of the ACM SIGMOD International Conference on Management of Data","author":"Agrawal R.","year":"1993","unstructured":"AgrawalR., ImielinskiT. and SwamiA., Mining associations between sets of items in massive databases, In BunemanP. and JajodiaS. 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