{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T14:29:05Z","timestamp":1781101745848,"version":"3.54.1"},"reference-count":37,"publisher":"IGI Global Scientific Publishing","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2014,7,1]]},"abstract":"<p>One of the most widely used algorithms to solve clustering problems is the K-means. Despite of the algorithm's timely performance to find a fairly good solution, it shows some drawbacks like its dependence on initial conditions and trapping in local minima. This paper proposes a novel hybrid algorithm, comprised of K-means and a variation operator inspired by mutation in evolutionary algorithms, called Noisy K-means Algorithm (NKA). Previous research used K-means as one of the genetic operators in Genetic Algorithms. However, the proposed NKA is a kind of individual based algorithm that combines advantages of both K-means and mutation. As a result, proposed NKA algorithm has the advantage of faster convergence time, while escaping from local optima. In this algorithm, a probability function is utilized which adaptively tunes the rate of mutation. Furthermore, a special mutation operator is used to guide the search process according to the algorithm performance. Finally, the proposed algorithm is compared with the classical K-means, SOM Neural Network, Tabu Search and Genetic Algorithm in a given set of data. Simulation results statistically demonstrate that NKA out-performs all others and it is prominently prone to real time clustering.<\/p>","DOI":"10.4018\/ijdwm.2014070101","type":"journal-article","created":{"date-parts":[[2014,10,16]],"date-time":"2014-10-16T08:03:38Z","timestamp":1413446618000},"page":"1-14","source":"Crossref","is-referenced-by-count":4,"title":["A Novel Hybrid Algorithm Based on K-Means and Evolutionary Computations for Real Time Clustering"],"prefix":"10.4018","volume":"10","author":[{"given":"Taha","family":"Mansouri","sequence":"first","affiliation":[{"name":"Department of Industrial Management, Allameh Tabataba'i University, Tehran, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ahad Zare","family":"Ravasan","sequence":"additional","affiliation":[{"name":"Department of Industrial Management, Allameh Tabataba'i University, Tehran, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammad Reza","family":"Gholamian","sequence":"additional","affiliation":[{"name":"School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2432","reference":[{"key":"ijdwm.2014070101-0","unstructured":"Agrawal, R., Mannila, H., Srikant, R., Toivonen, H., & Verkamo, A. I. (1996). Fast discovery of association rules. Advances in Knowledge Discovery and Data Mining, 12, 307-328."},{"key":"ijdwm.2014070101-1","doi-asserted-by":"publisher","DOI":"10.1016\/j.datak.2007.03.016"},{"key":"ijdwm.2014070101-2","doi-asserted-by":"publisher","DOI":"10.1016\/0031-3203(95)00022-R"},{"key":"ijdwm.2014070101-3","author":"A.Berson","year":"2002","journal-title":"Building data mining applications for CRM"},{"key":"ijdwm.2014070101-4","doi-asserted-by":"publisher","DOI":"10.1016\/j.imavis.2010.10.002"},{"key":"ijdwm.2014070101-5","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2012.07.021"},{"issue":"6","key":"ijdwm.2014070101-6","first-page":"866","article-title":"Data mining: An overview from a database perspective.","volume":"8","author":"M.-S.Chen","year":"1996","journal-title":"IEEE Transactions on"},{"key":"ijdwm.2014070101-7","author":"D.Cross","year":"1998","journal-title":"Fundamentals of queuing theory"},{"key":"ijdwm.2014070101-8","first-page":"543","author":"O.Daly","year":"2004","journal-title":"Exception rules mining based on negative association rules. Computational Science and Its Applications (ICCSA 2004)"},{"key":"ijdwm.2014070101-9","doi-asserted-by":"publisher","DOI":"10.1016\/j.amc.2007.02.029"},{"issue":"7","key":"ijdwm.2014070101-10","first-page":"3177","article-title":"A new hybrid algorithm based on PSO, SA, and K-means for cluster analysis.","volume":"6","author":"B. B.Firouzi","year":"2010","journal-title":"International Journal of Innovative Computing, Information, & Control"},{"key":"ijdwm.2014070101-11","doi-asserted-by":"publisher","DOI":"10.1016\/j.amc.2006.05.166"},{"key":"ijdwm.2014070101-12","doi-asserted-by":"publisher","DOI":"10.1109\/4235.771164"},{"key":"ijdwm.2014070101-13","author":"D. J.Hand","year":"2005","journal-title":"Principles of data mining"},{"key":"ijdwm.2014070101-14","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2009.09.011"},{"key":"ijdwm.2014070101-15","doi-asserted-by":"publisher","DOI":"10.1145\/331499.331504"},{"issue":"5","key":"ijdwm.2014070101-16","first-page":"758","article-title":"Fuzzy ants and clustering.","volume":"37","author":"P. M.Kanade","year":"2007","journal-title":"IEEE Transactions on"},{"key":"ijdwm.2014070101-17","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2007.01.028"},{"key":"ijdwm.2014070101-18","author":"L.Kaufman","year":"1990","journal-title":"Finding groups in data. An introduction to cluster analysis"},{"key":"ijdwm.2014070101-19","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2004.04.007"},{"key":"ijdwm.2014070101-20","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-45706-2_48"},{"key":"ijdwm.2014070101-21","doi-asserted-by":"publisher","DOI":"10.1002\/0471687545"},{"key":"ijdwm.2014070101-22","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2007.08.006"},{"key":"ijdwm.2014070101-23","doi-asserted-by":"publisher","DOI":"10.1016\/S0031-3203(02)00060-2"},{"key":"ijdwm.2014070101-24","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2010.09.084"},{"key":"ijdwm.2014070101-25","doi-asserted-by":"publisher","DOI":"10.1016\/S0031-3203(99)00137-5"},{"key":"ijdwm.2014070101-26","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2009.01.011"},{"key":"ijdwm.2014070101-27","doi-asserted-by":"publisher","DOI":"10.1016\/S0031-3203(02)00021-3"},{"key":"ijdwm.2014070101-28","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2008.07.016"},{"key":"ijdwm.2014070101-29","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2009.07.001"},{"key":"ijdwm.2014070101-30","doi-asserted-by":"publisher","DOI":"10.1631\/jzus.A0820196"},{"key":"ijdwm.2014070101-31","doi-asserted-by":"publisher","DOI":"10.1016\/j.csda.2004.12.004"},{"key":"ijdwm.2014070101-32","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2007.01.001"},{"key":"ijdwm.2014070101-33","doi-asserted-by":"publisher","DOI":"10.1016\/0031-3203(91)90097-O"},{"key":"ijdwm.2014070101-34","doi-asserted-by":"publisher","DOI":"10.1016\/j.aca.2003.12.032"},{"key":"ijdwm.2014070101-35","author":"J. T.Tou","year":"1974","journal-title":"Pattern recognition principles"},{"key":"ijdwm.2014070101-36","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2008.02.014"}],"container-title":["International Journal of Data Warehousing and Mining"],"original-title":[],"language":"ng","link":[{"URL":"https:\/\/www.igi-global.com\/viewtitle.aspx?TitleId=116890","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,1]],"date-time":"2022-06-01T11:55:00Z","timestamp":1654084500000},"score":1,"resource":{"primary":{"URL":"https:\/\/services.igi-global.com\/resolvedoi\/resolve.aspx?doi=10.4018\/ijdwm.2014070101"}},"subtitle":[""],"short-title":[],"issued":{"date-parts":[[2014,7,1]]},"references-count":37,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2014,7]]}},"URL":"https:\/\/doi.org\/10.4018\/ijdwm.2014070101","relation":{},"ISSN":["1548-3924","1548-3932"],"issn-type":[{"value":"1548-3924","type":"print"},{"value":"1548-3932","type":"electronic"}],"subject":[],"published":{"date-parts":[[2014,7,1]]}}}