{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:43:25Z","timestamp":1777704205466,"version":"3.51.4"},"reference-count":10,"publisher":"SAGE Publications","issue":"3","license":[{"start":{"date-parts":[[2018,6,14]],"date-time":"2018-06-14T00:00:00Z","timestamp":1528934400000},"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":[[2018,10]]},"abstract":"<jats:p>In previous studies, due to the sparsity and chaos of distributed data, such a result would lead to a local convergence phenomenon by using PSO algorithm, resulting in low accuracy of data mining. So this time we proposed a data mining algorithm based on neural network and particle swarm optimization. At the beginning, we calculated the global kernel function of differentiated distributed data mining and mixed to build the mining decision model. The training error was used as the constraint condition of mining optimization to realized data optimization mining. The results showed that the differential distributed data mining with this algorithm has higher accuracy and stronger convergence.<\/jats:p>","DOI":"10.3233\/jifs-169647","type":"journal-article","created":{"date-parts":[[2018,6,15]],"date-time":"2018-06-15T13:15:15Z","timestamp":1529068515000},"page":"2921-2926","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":12,"title":["Research on data mining algorithm based on neural network and particle swarm optimization"],"prefix":"10.1177","volume":"35","author":[{"given":"Xianju","family":"Fei","sequence":"first","affiliation":[{"name":"School of Computer Information and Engineering, Changzhou Institute of Technology, Jiangsu, China"}]},{"given":"Guozhong","family":"Tian","sequence":"additional","affiliation":[{"name":"School of Computer Information and Engineering, Changzhou Institute of Technology, Jiangsu, China"}]}],"member":"179","published-online":{"date-parts":[[2018,6,14]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1007\/s12665-015-4274-1"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2014.10.065"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1049\/iet-rpg.2015.0010"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2015.06.012"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00603-014-0569-x"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.2298\/CSIS141108031L"},{"key":"e_1_3_2_8_2","first-page":"1","article-title":"An efficient algorithm basedon artificial neural networks and particle swarm optimization for solution of nonlinear Troesch\u2019s problem","author":"Yadav N.","year":"2015","unstructured":"YadavN., YadavA., KumarM.et al., An efficient algorithm basedon artificial neural networks and particle swarm optimization for solution of nonlinear Troesch\u2019s problem, Neural Computing & Applications (2015), 1\u20138.","journal-title":"Neural Computing & Applications"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.4018\/JITR.2017070104"},{"issue":"4","key":"e_1_3_2_10_2","first-page":"1","article-title":"Fuzzy min\u2013max neural network andparticle swarm optimization based intrusion detection system","volume":"23","author":"Azad C.","year":"2016","unstructured":"AzadC. and JhaV.K., Fuzzy min\u2013max neural network andparticle swarm optimization based intrusion detection system, Microsystem Technologies23(4) (2016), 1\u201312.","journal-title":"Microsystem Technologies"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.2112\/SI73-119.1"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JIFS-169647","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.3233\/JIFS-169647","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JIFS-169647","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:40:42Z","timestamp":1777455642000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.3233\/JIFS-169647"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,6,14]]},"references-count":10,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2018,10]]}},"alternative-id":["10.3233\/JIFS-169647"],"URL":"https:\/\/doi.org\/10.3233\/jifs-169647","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,6,14]]}}}