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In the first step, the k-means clustering algorithm is applied to find the seed_points to build the granular structure of the spam and non-spam patterns. Moreover, the key part of the spam and non-spam classifiers\u2019 structure is captured by applying the interval analysis through the high homogeneity of the patterns. In the second step, PSO algorithm is hybridized with the k-means to optimize the formalized information granules\u2019 performance. The size of the hyperboxes is expanded away from the center of the granules by PSO. There are some patterns that do not placed in any of the created clusters and known as noise points. In the third step, the membership function in fuzzy sets is applied to solve the noise points\u2019 problem by allocating the noise points through the membership grades. The proposed model is evaluated based on the accuracy, misclassification and coverage criteria. Experimental results reveal that the performance of our proposed model is increased through applying Particle Swarm Optimization and fuzzy set.<\/jats:p>","DOI":"10.3233\/jifs-169133","type":"journal-article","created":{"date-parts":[[2016,12,23]],"date-time":"2016-12-23T16:45:21Z","timestamp":1482511521000},"page":"1355-1363","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":8,"title":["Fuzzy granular classifier approach for spam detection"],"prefix":"10.1177","volume":"32","author":[{"given":"Saber","family":"Salehi","sequence":"first","affiliation":[{"name":"UTM-IRDA Digital Media Centre of Excellence, Universiti Teknologi Malaysia, Skudai, Johor, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ali","family":"Selamat","sequence":"additional","affiliation":[{"name":"UTM-IRDA Digital Media Centre of Excellence, Universiti Teknologi Malaysia, Skudai, Johor, Malaysia"},{"name":"Faculty of Computing, Universiti Teknologi Malaysia, Skudai, Johor, Malaysia"},{"name":"University of Hradec Kralove, FIM, Center for Basic and Applied Research, Rokitanskeho, Hradec Kralove, Czech Republic"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kamil","family":"Kuca","sequence":"additional","affiliation":[{"name":"University of Hradec Kralove, FIM, Center for Basic and Applied Research, Rokitanskeho, Hradec Kralove, Czech Republic"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ondrej","family":"Krejcar","sequence":"additional","affiliation":[{"name":"University of Hradec Kralove, FIM, Center for Basic and Applied Research, Rokitanskeho, Hradec Kralove, Czech Republic"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thabit","family":"Sabbah","sequence":"additional","affiliation":[{"name":"Faculty of Computing, Universiti Teknologi Malaysia, Skudai, Johor, Malaysia"},{"name":"Faculty of Technology and Applied Sciences, Al Quds Open University (QOU), AL-Bireh, Rammallah, Palestine"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2016,12,23]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2014.05.002"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2012.01.022"},{"key":"e_1_3_2_4_2","first-page":"2673","article-title":"Spam filtering using statistical data compression models","volume":"7","author":"Bratko A.","year":"2006","unstructured":"BratkoA., Filipi\u010dB., CormackG.V., LynamT.R. and ZupanB., Spam filtering using statistical data compression models, The Journal of Machine Learning Research7 (2006), 2673\u20132698.","journal-title":"The Journal of Machine Learning Research"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1145\/1290958.1290968"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/S1353-4858(09)70007-8"},{"key":"e_1_3_2_7_2","unstructured":"Symantec Announces Message Labs Intelligence 2010 Annual Security Report December 7 2010 https:\/\/www.symantec.com\/about\/newsroom\/press-releases\/2010\/symantec_1207_01"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2014.02.114"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2014.03.015"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jss.2012.06.027"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2009.05.005"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICSESS.2011.5982390"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.biosystems.2008.02.006"},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.1109\/MySEC.2011.6140655"},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.dss.2006.11.012"},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2004.07.004"},{"key":"e_1_3_2_17_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2014.02.011"},{"key":"e_1_3_2_18_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2014.12.017"},{"key":"e_1_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2008.06.004"},{"key":"e_1_3_2_20_2","first-page":"202","article-title":"Self-nonself discrimination in a computer","author":"Forrest S.","year":"1994","unstructured":"ForrestS., PerelsonA.S., AllenL., CherukuriR., Self-nonself discrimination in a computer, 2012 IEEE Symposium on Security and Privacy, IEEE Computer Society, 1994, pp. 202\u2013202.","journal-title":"2012 IEEE Symposium on Security and Privacy, IEEE Computer Society"},{"key":"e_1_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-45105-6_28"},{"key":"e_1_3_2_22_2","unstructured":"AsuncionA. and NewmanD. 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