{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T15:50:37Z","timestamp":1775663437731,"version":"3.50.1"},"reference-count":38,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2015,4,28]],"date-time":"2015-04-28T00:00:00Z","timestamp":1430179200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Soft Comput"],"published-print":{"date-parts":[[2016,8]]},"DOI":"10.1007\/s00500-015-1686-5","type":"journal-article","created":{"date-parts":[[2015,4,27]],"date-time":"2015-04-27T03:31:07Z","timestamp":1430105467000},"page":"3163-3176","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Artificial bee colony algorithm for clustering: an extreme learning approach"],"prefix":"10.1007","volume":"20","author":[{"given":"Abobakr Khalil","family":"Alshamiri","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alok","family":"Singh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bapi Raju","family":"Surampudi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2015,4,28]]},"reference":[{"key":"1686_CR1","doi-asserted-by":"crossref","unstructured":"Alshamiri AK, Singh A, Surampudi BR (2014) A novel elm k-means algorithm for clustering. In: Proceedings of 5th joint international conference on swarm, evolutionary and memetic computing (SEMCCO 2014) and fuzzy and neural computing (FANCCO 2014), Odisha, India (To appear)","DOI":"10.1007\/978-3-319-20294-5_19"},{"issue":"5","key":"1686_CR2","doi-asserted-by":"crossref","first-page":"801","DOI":"10.1109\/TPAMI.2005.88","volume":"27","author":"F Camastra","year":"2005","unstructured":"Camastra F, Verri A (2005) A novel kernel method for clustering. IEEE Trans Pattern Anal Mach Intell 27(5):801\u2013805","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"1686_CR3","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.ins.2015.01.012","volume":"302","author":"SN Chaurasia","year":"2015","unstructured":"Chaurasia SN, Singh A (2015) A hybrid swarm intelligence approach to the registration area planning problem. Inform Sci 302:50\u201369","journal-title":"Inform Sci"},{"key":"1686_CR4","doi-asserted-by":"crossref","unstructured":"Chitta R, Jin R, Havens TC, Jain AK (2011) Approximate kernel k-means: solution to large scale kernel clustering. In: Proceedings of 17th ACM SIGKDD international conference on knowledge discovery and data mining (KDD), New York, USA, pp 895\u2013903","DOI":"10.1145\/2020408.2020558"},{"key":"1686_CR5","volume-title":"Genetic algorithm and grouping problems","author":"E Falkenauer","year":"1998","unstructured":"Falkenauer E (1998) Genetic algorithm and grouping problems. Wiley, New York"},{"key":"1686_CR6","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1016\/j.patcog.2007.05.018","volume":"41","author":"M Filippone","year":"2008","unstructured":"Filippone M, Camastra F, Masulli F, Rovetta S (2008) A survey of kernel and spectral methods for clustering. Pattern Recognit 41:176\u2013190","journal-title":"Pattern Recognit"},{"key":"1686_CR7","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1111\/j.1469-1809.1936.tb02137.x","volume":"7","author":"RA Fisher","year":"1936","unstructured":"Fisher RA (1936) The use of multiple measurements in taxonomic problems. Ann Eugen 7:179\u2013188","journal-title":"Ann Eugen"},{"issue":"3","key":"1686_CR8","doi-asserted-by":"crossref","first-page":"780","DOI":"10.1109\/TNN.2002.1000150","volume":"13","author":"M Girolami","year":"2002","unstructured":"Girolami M (2002) Mercer kernel based clustering in feature space. IEEE Trans Neural Netw 13(3):780\u2013784","journal-title":"IEEE Trans Neural Netw"},{"key":"1686_CR9","unstructured":"Han J, Kamber M (2001) Data mining: concepts and techniques. Academic Press, San Diego"},{"key":"1686_CR10","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.neucom.2012.12.063","volume":"128","author":"Q He","year":"2014","unstructured":"He Q, Jin X, Du C, Zhuang F, Shi Z (2014) Clustering in extreme learning machine feature space. Neurocomputing 128:88\u201395","journal-title":"Neurocomputing"},{"issue":"2","key":"1686_CR11","doi-asserted-by":"crossref","first-page":"274","DOI":"10.1109\/TNN.2003.809401","volume":"14","author":"G-B Huang","year":"2003","unstructured":"Huang G-B (2003) Learning capability and storage capacity of two-hidden-layer feedforward networks. IEEE Trans Neural Netw 14(2):274\u2013281","journal-title":"IEEE Trans Neural Netw"},{"key":"1686_CR12","unstructured":"Huang G-B, Zhu Q-Y, Siew C-K (2004) Extreme learning machine: a new learning scheme of feedforward neural networks. In: Proceedings of international joint conference on neural networks (IJCNN), vol 2. Budapest, Hungary, pp 985\u2013990"},{"issue":"4","key":"1686_CR13","doi-asserted-by":"crossref","first-page":"879","DOI":"10.1109\/TNN.2006.875977","volume":"17","author":"G-B Huang","year":"2006","unstructured":"Huang G-B, Chen L, Siew C-K (2006a) Universal approximation using incremental constructive feedforward networks with random hidden nodes. IEEE Trans Neural Netw 17(4):879\u2013892","journal-title":"IEEE Trans Neural Netw"},{"key":"1686_CR14","doi-asserted-by":"crossref","first-page":"489","DOI":"10.1016\/j.neucom.2005.12.126","volume":"70","author":"G-B Huang","year":"2006","unstructured":"Huang G-B, Zhu Q-Y, Siew C-K (2006b) Extreme learning machine: theory and applications. Neurocomputing 70:489\u2013501","journal-title":"Neurocomputing"},{"issue":"2","key":"1686_CR15","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1109\/TSMCB.2011.2168604","volume":"42","author":"G-B Huang","year":"2012","unstructured":"Huang G-B, Zhou H, Ding X, Zhang R (2012) Extreme learning machine for regression and multiclass classification. IEEE Trans Syst Man Cybern B Cybern 42(2):513\u2013529","journal-title":"IEEE Trans Syst Man Cybern B Cybern"},{"key":"1686_CR16","doi-asserted-by":"crossref","first-page":"651","DOI":"10.1016\/j.patrec.2009.09.011","volume":"31","author":"AK Jain","year":"2010","unstructured":"Jain AK (2010) Data clustering: 50 years beyond k-means. Pattern Recognit 31:651\u2013666","journal-title":"Pattern Recognit"},{"key":"1686_CR17","volume-title":"Algorithms for clustering data","author":"AK Jain","year":"1989","unstructured":"Jain AK, Dubes RC (1989) Algorithms for clustering data. Prentice-Hall, Englewood Cliffs"},{"issue":"3","key":"1686_CR18","doi-asserted-by":"crossref","first-page":"264","DOI":"10.1145\/331499.331504","volume":"31","author":"AK Jain","year":"1999","unstructured":"Jain AK, Murty MN, Flynn PJ (1999) Data clustering: a review. ACM Comput Surv 31(3):264\u2013323","journal-title":"ACM Comput Surv"},{"key":"1686_CR19","unstructured":"Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. In: Technical report-TR06. Erciyes University, Engineering Faculty, Computer Engineering Department, Turkey"},{"key":"1686_CR20","doi-asserted-by":"crossref","unstructured":"Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (abc) algorithm. J Glob Optim 39(3):459\u2013471","DOI":"10.1007\/s10898-007-9149-x"},{"key":"1686_CR21","doi-asserted-by":"crossref","first-page":"652","DOI":"10.1016\/j.asoc.2009.12.025","volume":"11","author":"D Karaboga","year":"2010","unstructured":"Karaboga D, Ozturk C (2010) A novel clustering approach: artificial bee colony (abc) algorithm. Appl Soft Comput 11:652\u2013657","journal-title":"Appl Soft Comput"},{"issue":"3","key":"1686_CR22","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1109\/3477.764879","volume":"29","author":"K Krishna","year":"1999","unstructured":"Krishna K, Murty MN (1999) Genetic k-means algorithm. IEEE Trans Syst Man Cybern B Cybern 29(3):433\u2013439","journal-title":"IEEE Trans Syst Man Cybern B Cybern"},{"key":"1686_CR23","doi-asserted-by":"crossref","first-page":"3191","DOI":"10.1016\/j.neucom.2010.05.022","volume":"73","author":"Y Lan","year":"2010","unstructured":"Lan Y, Soh YC, Huang G-B (2010) Constructive hidden nodes selection of extreme learning machine for regression. Neurocomputing 73:3191\u20133199","journal-title":"Neurocomputing"},{"key":"1686_CR24","doi-asserted-by":"crossref","first-page":"515","DOI":"10.1016\/S0031-3203(99)00057-6","volume":"33","author":"MK Ng","year":"2000","unstructured":"Ng MK (2000) A note on constrained k-means algorithms. Pattern Recogn 33:515\u2013519","journal-title":"Pattern Recogn"},{"issue":"5","key":"1686_CR25","doi-asserted-by":"crossref","first-page":"1299","DOI":"10.1162\/089976698300017467","volume":"10","author":"B Scholkopf","year":"1998","unstructured":"Scholkopf B, Smola A, Muller K (1998) Nonlinear component analysis as a kernel eigenvalue problem. Neural Comput 10(5):1299\u20131319","journal-title":"Neural Comput"},{"issue":"10","key":"1686_CR26","doi-asserted-by":"crossref","first-page":"1003","DOI":"10.1016\/0031-3203(91)90097-O","volume":"24","author":"SZ Selim","year":"1991","unstructured":"Selim SZ, Al-sultan K (1991) A simulated annealing algorithm for the clustering problems. Pattern Recogn 24(10):1003\u20131008","journal-title":"Pattern Recogn"},{"key":"1686_CR27","volume-title":"Matrices: theory and applications","author":"D Serre","year":"2002","unstructured":"Serre D (2002) Matrices: theory and applications. Springer, NewYork"},{"key":"1686_CR28","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1016\/j.aca.2003.12.032","volume":"509","author":"P Shelokar","year":"2004","unstructured":"Shelokar P, Jayaraman V, Kulkarni B (2004) An ant colony approach for clustering. Analytica Chimica Acta 509:187\u2013195","journal-title":"Analytica Chimica Acta"},{"key":"1686_CR29","doi-asserted-by":"crossref","unstructured":"Sundar S, Singh A (2010) A swarm intelligence approach to the quadratic multiple knapsack problem. In: Proceedings of the 17th international conference on neural information processing (ICONIP 2010). Lecture notes in computer science, vol 6443, pp 626\u2013633","DOI":"10.1007\/978-3-642-17537-4_76"},{"key":"1686_CR30","doi-asserted-by":"publisher","unstructured":"Sundar S, Singh A (2014) Metaheuristic approaches for the blockmodel problem. IEEE Syst J. doi: 10.1109\/JSYST.2014.2342931","DOI":"10.1109\/JSYST.2014.2342931"},{"issue":"7","key":"1686_CR31","doi-asserted-by":"crossref","first-page":"1181","DOI":"10.1109\/TNN.2009.2019722","volume":"20","author":"GF Tzortzis","year":"2009","unstructured":"Tzortzis GF, Likas AC (2009) The global kernel k-means algorithm for clustering in feature space. IEEE Trans Neural Netw 20(7):1181\u20131194","journal-title":"IEEE Trans Neural Netw"},{"key":"1686_CR32","doi-asserted-by":"crossref","unstructured":"van der Merwe D, Engelhrecht A (2003) Data clustering using particle swarm optimization. In: Proceedings of IEEE congress on evolutionary computation (CEC 03). Canbella, Australia, pp 215\u2013220","DOI":"10.1109\/CEC.2003.1299577"},{"key":"1686_CR33","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.asoc.2014.09.029","volume":"26","author":"P Venkatesh","year":"2015","unstructured":"Venkatesh P, Singh A (2015) Two metaheuristic approaches for the multiple traveling salesperson problem. Appl Soft Comput 26:74\u201389","journal-title":"Appl Soft Comput"},{"key":"1686_CR34","doi-asserted-by":"crossref","unstructured":"Xu R, Wunsch II D (2005) Survey of clustering algorithms. IEEE Trans Neural Netw 16(3):645\u2013678","DOI":"10.1109\/TNN.2005.845141"},{"key":"1686_CR35","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/j.neucom.2012.04.025","volume":"97","author":"X Yan","year":"2012","unstructured":"Yan X, Zhu Y, Zou W, Wang L (2012) A new approach for data clustering using hybrid artificial bee colony algorithm. Neurocomputing 97:241\u2013250","journal-title":"Neurocomputing"},{"key":"1686_CR36","doi-asserted-by":"crossref","first-page":"4658","DOI":"10.1016\/j.ins.2010.11.005","volume":"181","author":"L Zhang","year":"2011","unstructured":"Zhang L, Cao Q (2011) A novel ant-based clustering algorithm using the kernel method. Inform Sci 181:4658\u20134672","journal-title":"Inform Sci"},{"key":"1686_CR37","doi-asserted-by":"crossref","unstructured":"Zhang R, Rudnicky AI (2002) A large scale clustering scheme for kernel k-means. In: Proceedings of 16th international conference on pattern recognition (ICPR), vol 4, Quebec, Canada, pp 289\u2013292","DOI":"10.1109\/ICPR.2002.1047453"},{"key":"1686_CR38","doi-asserted-by":"crossref","first-page":"4761","DOI":"10.1016\/j.eswa.2009.11.003","volume":"37","author":"C Zhang","year":"2010","unstructured":"Zhang C, Ouyang D, Ning J (2010) An artificial bee colony approach for clustering. Expert Syst Appl 37:4761\u20134767","journal-title":"Expert Syst Appl"}],"container-title":["Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-015-1686-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s00500-015-1686-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-015-1686-5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,9]],"date-time":"2023-08-09T20:28:33Z","timestamp":1691612913000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s00500-015-1686-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015,4,28]]},"references-count":38,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2016,8]]}},"alternative-id":["1686"],"URL":"https:\/\/doi.org\/10.1007\/s00500-015-1686-5","relation":{},"ISSN":["1432-7643","1433-7479"],"issn-type":[{"value":"1432-7643","type":"print"},{"value":"1433-7479","type":"electronic"}],"subject":[],"published":{"date-parts":[[2015,4,28]]}}}