{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,8]],"date-time":"2025-09-08T06:17:04Z","timestamp":1757312224637,"version":"3.37.3"},"reference-count":82,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2024,7,17]],"date-time":"2024-07-17T00:00:00Z","timestamp":1721174400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,7,17]],"date-time":"2024-07-17T00:00:00Z","timestamp":1721174400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SN COMPUT. SCI."],"DOI":"10.1007\/s42979-024-03048-0","type":"journal-article","created":{"date-parts":[[2024,7,17]],"date-time":"2024-07-17T20:25:56Z","timestamp":1721247956000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["An Improved Water Flow Optimizer for Data Clustering"],"prefix":"10.1007","volume":"5","author":[{"given":"Prateek","family":"Thakral","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3451-4897","authenticated-orcid":false,"given":"Yugal","family":"Kumar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,7,17]]},"reference":[{"key":"3048_CR1","doi-asserted-by":"publisher","DOI":"10.1201\/b17320","volume-title":"Data clustering algorithms and applications","author":"CC Aggarwal","year":"2014","unstructured":"Aggarwal CC, Reddy CK. Data clustering algorithms and applications. Londra: Chapman & Hall\/CRC Data mining and Knowledge Discovery Series; 2014."},{"key":"3048_CR2","doi-asserted-by":"crossref","unstructured":"Gan G, Ma C, Wu J. Data clustering: theory, algorithms, and applications. Society for Industrial and Applied Mathematics; 2020.","DOI":"10.1137\/1.9781611976335"},{"issue":"1","key":"3048_CR3","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1385\/MB:31:1:055","volume":"31","author":"Y Zhao","year":"2005","unstructured":"Zhao Y, Karypis G. Data clustering in life sciences. Mol Biotechnol. 2005;31(1):55\u201380.","journal-title":"Mol Biotechnol"},{"key":"3048_CR4","doi-asserted-by":"crossref","unstructured":"Aggarwal CC, Reddy CK. An introduction to cluster analysis. 2013.","DOI":"10.1007\/978-1-4614-6396-2_1"},{"key":"3048_CR5","doi-asserted-by":"crossref","unstructured":"G. V. P. S. D. a. S. D. Brock. ClValid: an R package for cluster validation. J Stat Softw. 2008;25(4):1\u201322.","DOI":"10.18637\/jss.v025.i04"},{"key":"3048_CR6","unstructured":"M. Y. B. a. M. V. Halkidi. On clustering validation techniques. J Intell Inf Syst."},{"issue":"1","key":"3048_CR7","doi-asserted-by":"publisher","first-page":"759","DOI":"10.1007\/s12065-020-00562-x","volume":"15","author":"A Kaur","year":"2022","unstructured":"Kaur A, Kumar Y. A new metaheuristic algorithm based on water wave optimization for data clustering. Evol Intel. 2022;15(1):759\u201383.","journal-title":"Evol Intel"},{"key":"3048_CR8","doi-asserted-by":"publisher","first-page":"429","DOI":"10.1007\/978-3-319-04960-1_38","volume-title":"Advances in signal processing and intelligent recognition systems","author":"AJ Sahoo","year":"2014","unstructured":"Sahoo AJ, Kumar Y. Modified teacher learning based optimization method for data clustering. In: Advances in signal processing and intelligent recognition systems. Berlin: Springer International Publishing; 2014. p. 429\u201337."},{"key":"3048_CR9","doi-asserted-by":"crossref","unstructured":"Kumar Y, Gupta S, Kumar D, Sahoo G. A clustering approach based on charged particles. In: Optimization algorithms-methods and applications. 2016. p. 245\u201363.","DOI":"10.5772\/63081"},{"issue":"1","key":"3048_CR10","doi-asserted-by":"publisher","first-page":"209","DOI":"10.1007\/s10044-021-01052-1","volume":"25","author":"A Kaur","year":"2022","unstructured":"Kaur A, Kumar Y. A multi-objective vibrating particle system algorithm for data clustering. Pattern Anal Appl. 2022;25(1):209\u201339.","journal-title":"Pattern Anal Appl"},{"key":"3048_CR11","doi-asserted-by":"publisher","first-page":"664","DOI":"10.1016\/j.neucom.2017.06.053","volume":"267","author":"A Saxena","year":"2017","unstructured":"Saxena A, Prasad M, Gupta A, Bharill N, Patel OP, Tiwari A, Lin CT. A review of clustering techniques and developments. Neurocomputing. 2017;267:664\u201381.","journal-title":"Neurocomputing"},{"key":"3048_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.knosys.2017.05.023","volume":"130","author":"L \u00d6zbak\u0131r","year":"2017","unstructured":"\u00d6zbak\u0131r L, Turna F. Clustering performance comparison of new generation meta-heuristic algorithms. Knowl-Based Syst. 2017;130:1\u201316.","journal-title":"Knowl-Based Syst"},{"key":"3048_CR13","doi-asserted-by":"crossref","unstructured":"Patel KMA, Thakral P. The best clustering algorithms in data mining. In: International Conference on Communication and Signal Processing (ICCSP). 2016. p. 2042\u20136.","DOI":"10.1109\/ICCSP.2016.7754534"},{"issue":"7","key":"3048_CR14","doi-asserted-by":"publisher","first-page":"709","DOI":"10.1016\/j.neunet.2011.03.020","volume":"24","author":"R Xu","year":"2011","unstructured":"Xu R, Wunsch DC II. BARTMAP: a viable structure for biclustering. Neural Netw. 2011;24(7):709\u201316.","journal-title":"Neural Netw"},{"issue":"4","key":"3048_CR15","doi-asserted-by":"publisher","first-page":"751","DOI":"10.1109\/TKDE.2011.221","volume":"25","author":"B Jiang","year":"2011","unstructured":"Jiang B, Pei J, Tao Y, Lin X. Clustering uncertain data based on probability distribution similarity. IEEE Trans Knowl Data Eng. 2011;25(4):751\u201363.","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"4","key":"3048_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2742642","volume":"47","author":"A Mukhopadhyay","year":"2015","unstructured":"Mukhopadhyay A, Maulik U, Bandyopadhyay S. A survey of multi objective evolutionary clustering. ACM Comput Surv (CSUR). 2015;47(4):1\u201346.","journal-title":"ACM Comput Surv (CSUR)"},{"issue":"3","key":"3048_CR17","doi-asserted-by":"publisher","first-page":"1507","DOI":"10.1007\/s11042-013-1655-x","volume":"73","author":"X Sevillano","year":"2014","unstructured":"Sevillano X, Al\u00edas F. A one-shot domain-independent robust multimedia clustering methodology based on hybrid multimodal fusion. Multimed Tools Appl. 2014;73(3):1507\u201343.","journal-title":"Multimed Tools Appl"},{"key":"3048_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2022.104743","volume":"110","author":"AE Ezugwu","year":"2022","unstructured":"Ezugwu AE, Ikotun AM, Oyelade OO, Abualigah L, Agushaka JO, Eke CI, Akinyelu AA. A comprehensive survey of clustering algorithms: state-of-the-art machine learning applications, taxonomy, challenges, and future research prospects. Eng Appl Artif Intell. 2022;110: 104743.","journal-title":"Eng Appl Artif Intell"},{"key":"3048_CR19","unstructured":"MacQueen J. Some methods for classification and analysis of multivariate observations. In: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, no. 14. 1967. p. 281\u201397."},{"issue":"3","key":"3048_CR20","doi-asserted-by":"publisher","first-page":"264","DOI":"10.1145\/331499.331504","volume":"31","author":"AK Jain","year":"1999","unstructured":"Jain AK, Murty MN, Flynn PJ. Data clustering: a review. ACM Comput Surv (CSUR). 1999;31(3):264\u2013323.","journal-title":"ACM Comput Surv (CSUR)"},{"issue":"1","key":"3048_CR21","doi-asserted-by":"publisher","first-page":"1582","DOI":"10.1016\/j.eswa.2011.07.123","volume":"39","author":"T Cura","year":"2012","unstructured":"Cura T. A particle swarm optimization approach to clustering. Expert Syst Appl. 2012;39(1):1582\u20138.","journal-title":"Expert Syst Appl"},{"key":"3048_CR22","doi-asserted-by":"publisher","first-page":"401","DOI":"10.1016\/j.asoc.2014.10.026","volume":"26","author":"AR Jordehi","year":"2015","unstructured":"Jordehi AR. Enhanced leader PSO (ELPSO): a new PSO variant for solving global optimisation problems. Appl Soft Comput. 2015;26:401\u201317.","journal-title":"Appl Soft Comput"},{"key":"3048_CR23","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1016\/j.patrec.2017.10.031","volume":"115","author":"N Kushwaha","year":"2018","unstructured":"Kushwaha N, Pant M, Kant S, Jain VK. Magnetic optimization algorithm for data clustering. Pattern Recogn Lett. 2018;115:59\u201365.","journal-title":"Pattern Recogn Lett"},{"issue":"2","key":"3048_CR24","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1007\/s13748-014-0049-2","volume":"2","author":"Y Kumar","year":"2014","unstructured":"Kumar Y, Sahoo G. A charged system search approach for data clustering. Prog Artif Intell. 2014;2(2):153\u201366.","journal-title":"Prog Artif Intell"},{"key":"3048_CR25","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1016\/j.ins.2012.08.023","volume":"222","author":"A Hatamlou","year":"2013","unstructured":"Hatamlou A. Black hole: a new heuristic optimization approach for data clustering. Inf Sci. 2013;222:175\u201384.","journal-title":"Inf Sci."},{"issue":"9","key":"3048_CR26","doi-asserted-by":"publisher","first-page":"10541","DOI":"10.1007\/s10489-021-02934-x","volume":"52","author":"A Kaur","year":"2022","unstructured":"Kaur A, Kumar Y. Neighborhood search based improved bat algorithm for data clustering. Appl Intell. 2022;52(9):10541\u201375.","journal-title":"Appl Intell"},{"key":"3048_CR27","unstructured":"Kumar Y, Kaur A. Variants of bat algorithm for solving partitional clustering problems. Eng Comput. 2021;1\u201327."},{"key":"3048_CR28","unstructured":"Karaboga D. An idea based on honey bee swarm for numerical optimization, vol 200, p. 1\u201310. Technical report-tr06, Erciyes University, engineering faculty, computer engineering department. 2005."},{"issue":"3","key":"3048_CR29","doi-asserted-by":"publisher","first-page":"459","DOI":"10.1007\/s10898-007-9149-x","volume":"39","author":"D Karaboga","year":"2007","unstructured":"Karaboga D, Basturk B. A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Glob Optim. 2007;39(3):459\u201371.","journal-title":"J Glob Optim"},{"issue":"4","key":"3048_CR30","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1109\/MCI.2006.329691","volume":"1","author":"M Dorigo","year":"2006","unstructured":"Dorigo M, Birattari M, Stutzle T. Artificial ants as a computational intelligence technique. IEEE Comput Intell Mag. 2006;1(4):28\u201339.","journal-title":"IEEE Comput Intell Mag"},{"issue":"2","key":"3048_CR31","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1016\/j.advengsoft.2005.04.005","volume":"37","author":"OK Erol","year":"2006","unstructured":"Erol OK, Eksin I. A new optimization method: big bang\u2013big crunch. Adv Eng Softw. 2006;37(2):106\u201311.","journal-title":"Adv Eng Softw"},{"key":"3048_CR32","doi-asserted-by":"crossref","unstructured":"Ergezer M, Simon D, Du D. Oppositional biogeography-based optimization. In: 2009 IEEE international conference on systems, man and cybernetics. IEEE; 2009. p. 1009\u201314.","DOI":"10.1109\/ICSMC.2009.5346043"},{"issue":"1","key":"3048_CR33","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1093\/comjnl\/bxz163","volume":"65","author":"A Osmani","year":"2022","unstructured":"Osmani A, Mohasefi JB, Gharehchopogh FS. Sentiment classification using two effective optimization methods derived from the artificial bee colony optimization and imperialist competitive algorithm. Comput J. 2022;65(1):18\u201366.","journal-title":"Comput J"},{"issue":"2","key":"3048_CR34","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1515\/fcds-2016-0006","volume":"41","author":"A Bouyer","year":"2016","unstructured":"Bouyer A. An optimized k-harmonic means algorithm combined with modified particle swarm optimization and cuckoo search algorithm. Found Comput Decis Sci. 2016;41(2):99\u2013121.","journal-title":"Found Comput Decis Sci"},{"issue":"12","key":"3048_CR35","doi-asserted-by":"publisher","first-page":"14555","DOI":"10.1016\/j.eswa.2011.05.027","volume":"38","author":"LY Chuang","year":"2011","unstructured":"Chuang LY, Hsiao CJ, Yang CH. Chaotic particle swarm optimization for data clustering. Expert Syst Appl. 2011;38(12):14555\u201363.","journal-title":"Expert Syst Appl"},{"key":"3048_CR36","doi-asserted-by":"publisher","first-page":"607","DOI":"10.1007\/978-3-642-55038-6_95","volume-title":"Future information technology","author":"M Zhao","year":"2014","unstructured":"Zhao M, Tang H, Guo J, Sun Y. Data clustering using particle swarm optimization. In: Future information technology. Berlin, Germany: Springer; 2014. p. 607\u201312."},{"issue":"12","key":"3048_CR37","doi-asserted-by":"publisher","first-page":"7189","DOI":"10.1007\/s13369-017-3049-2","volume":"43","author":"F Liu","year":"2018","unstructured":"Liu F, Sun Y, Wang GG, Wu T. An artificial bee colony algorithm based on dynamic penalty and L\u00e9vy flight for constrained optimization problems. Arab J Sci Eng. 2018;43(12):7189\u2013208.","journal-title":"Arab J Sci Eng"},{"key":"3048_CR38","doi-asserted-by":"publisher","first-page":"5189","DOI":"10.1007\/s11227-019-02786-w","volume":"75","author":"Z Du","year":"2019","unstructured":"Du Z, Han D, Li KC. Improving the performance of feature selection and data clustering with novel global search and elite-guided artificial bee colony algorithm. J Supercomput. 2019;75:5189\u2013226.","journal-title":"J Supercomput"},{"issue":"3","key":"3048_CR39","doi-asserted-by":"publisher","first-page":"1","DOI":"10.4018\/IJISMD.2019070101","volume":"10","author":"H Singh","year":"2019","unstructured":"Singh H, Kumar Y. Cellular automata based model for e-healthcare data analysis. Int J Inf Syst Model Design (IJISMD). 2019;10(3):1\u201318.","journal-title":"Int J Inf Syst Model Design (IJISMD)"},{"issue":"8","key":"3048_CR40","doi-asserted-by":"publisher","first-page":"7753","DOI":"10.1109\/TCYB.2021.3049607","volume":"52","author":"K Luo","year":"2021","unstructured":"Luo K. Water flow optimizer: a nature-inspired evolutionary algorithm for global optimization. IEEE Trans Cybern. 2021;52(8):7753\u201364.","journal-title":"IEEE Trans Cybern."},{"key":"3048_CR41","doi-asserted-by":"crossref","unstructured":"Matos Mac\u00eado FJ, da Rocha Neto AR. A binary water flow optimizer applied to feature selection. In: International Conference on Intelligent Data Engineering and Automated Learning. Cham: Springer; 2022. p. 94\u2013103.","DOI":"10.1007\/978-3-031-21753-1_10"},{"issue":"4","key":"3048_CR42","doi-asserted-by":"publisher","first-page":"627","DOI":"10.3390\/pr9040627","volume":"9","author":"M Said","year":"2021","unstructured":"Said M, Shaheen AM, Ginidi AR, El-Sehiemy RA, Mahmoud K, Lehtonen M, Darwish MM. Estimating parameters of photovoltaic models using accurate turbulent flow of water optimizer. Processes. 2021;9(4):627.","journal-title":"Processes"},{"key":"3048_CR43","doi-asserted-by":"crossref","unstructured":"Cheng MM, Zhang J, Wang DG, Tan W, Yang J. A localization algorithm based on improved water flow optimizer and max-similarity path for 3D heterogeneous wireless sensor networks. IEEE Sens J. 2023.","DOI":"10.1109\/JSEN.2023.3271820"},{"issue":"2","key":"3048_CR44","doi-asserted-by":"publisher","first-page":"1728","DOI":"10.1007\/s11227-023-05540-5","volume":"80","author":"A Qtaish","year":"2024","unstructured":"Qtaish A, Braik M, Albashish D, Alshammari MT, Alreshidi A, Alreshidi EJ. Optimization of K-means clustering method using hybrid capuchin search algorithm. J Supercomput. 2024;80(2):1728\u201387.","journal-title":"J Supercomput"},{"issue":"2","key":"3048_CR45","doi-asserted-by":"publisher","first-page":"991","DOI":"10.1007\/s00500-023-09367-3","volume":"28","author":"RJ Kuo","year":"2024","unstructured":"Kuo RJ, Hsu CC, Nguyen TPQ, Tsai CY. Hybrid multi-objective metaheuristic and possibilistic intuitionistic fuzzy c-means algorithms for cluster analysis. Soft Comput. 2024;28(2):991\u20131008.","journal-title":"Soft Comput"},{"issue":"1","key":"3048_CR46","doi-asserted-by":"publisher","first-page":"5434","DOI":"10.1038\/s41598-024-55619-z","volume":"14","author":"M Premkumar","year":"2024","unstructured":"Premkumar M, Sinha G, Ramasamy MD, Sahu S, Subramanyam CB, Sowmya R, et al. Augmented weighted K-means grey wolf optimizer: an enhanced metaheuristic algorithm for data clustering problems. Sci Rep. 2024;14(1):5434.","journal-title":"Sci Rep"},{"issue":"8","key":"3048_CR47","doi-asserted-by":"publisher","first-page":"10153","DOI":"10.1007\/s13369-022-07545-3","volume":"48","author":"H Demirci","year":"2023","unstructured":"Demirci H, Yurtay N, Yurtay Y, Zaimo\u011flu EA. Electrical search algorithm: a new metaheuristic algorithm for clustering problem. Arab J Sci Eng. 2023;48(8):10153\u201372.","journal-title":"Arab J Sci Eng"},{"issue":"4","key":"3048_CR48","doi-asserted-by":"publisher","first-page":"894","DOI":"10.3390\/sym15040894","volume":"15","author":"FS Harehchopogh","year":"2023","unstructured":"Harehchopogh FS, Khargoush AA. A chaotic-based interactive autodidactic school algorithm for data clustering problems and its application on COVID-19 disease detection. Symmetry. 2023;15(4):894.","journal-title":"Symmetry"},{"key":"3048_CR49","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2022.109838","volume":"132","author":"E Zorarpac\u0131","year":"2023","unstructured":"Zorarpac\u0131 E. Data clustering using leaders and followers optimization and differential evolution. Appl Soft Comput. 2023;132:109838.","journal-title":"Appl Soft Comput"},{"key":"3048_CR50","doi-asserted-by":"publisher","first-page":"434","DOI":"10.1016\/j.ins.2022.12.057","volume":"623","author":"Y Duan","year":"2023","unstructured":"Duan Y, Liu C, Li S, Guo X, Yang C. An automatic affinity propagation clustering based on improved equilibrium optimizer and t-SNE for high-dimensional data. Inf Sci. 2023;623:434\u201354.","journal-title":"Inf Sci"},{"key":"3048_CR51","unstructured":"Boroujeni SPH, Pashaei E. A hybrid chimp optimization algorithm and generalized normal distribution algorithm with opposition-based learning strategy for solving data clustering problems. 2023.\u00a0arXiv preprint http:\/\/arxiv.org\/abs\/2302.08623."},{"issue":"3","key":"3048_CR52","doi-asserted-by":"publisher","first-page":"4599","DOI":"10.1007\/s11042-022-13453-3","volume":"82","author":"H Singh","year":"2023","unstructured":"Singh H, Rai V, Kumar N, Dadheech P, Kotecha K, Selvachandran G, Abraham A. An enhanced whale optimization algorithm for clustering. Multimed Tools Appl. 2023;82(3):4599\u2013618.","journal-title":"Multimed Tools Appl"},{"issue":"2","key":"3048_CR53","doi-asserted-by":"publisher","first-page":"0409","DOI":"10.21123\/bsj.2022.19.2.0409","volume":"19","author":"HNK Al-Behadili","year":"2022","unstructured":"Al-Behadili HNK. Improved firefly algorithm with variable neighborhood search for data clustering. Baghdad Sci J. 2022;19(2):0409\u20130409.","journal-title":"Baghdad Sci J"},{"issue":"1","key":"3048_CR54","doi-asserted-by":"publisher","first-page":"2012000","DOI":"10.1080\/08839514.2021.2012000","volume":"36","author":"F Besharatnia","year":"2022","unstructured":"Besharatnia F, Talebpour A, Aliakbary S. An improved grey wolves optimization algorithm for dynamic community detection and data clustering. Appl Artif Intell. 2022;36(1):2012000.","journal-title":"Appl Artif Intell"},{"issue":"1","key":"3048_CR55","first-page":"1","volume":"13","author":"H Singh","year":"2022","unstructured":"Singh H, Kumar Y. An enhanced version of cat swarm optimization algorithm for cluster analysis. Int J Appl Metah Comput (IJAMC). 2022;13(1):1\u201325.","journal-title":"Int J Appl Metah Comput (IJAMC)"},{"issue":"7","key":"3048_CR56","doi-asserted-by":"publisher","DOI":"10.1111\/exsy.12491","volume":"39","author":"N Kushwaha","year":"2022","unstructured":"Kushwaha N, Pant M, Sharma S. Electromagnetic optimization based clustering algorithm. Expert Syst. 2022;39(7): e12491.","journal-title":"Expert Syst"},{"issue":"4","key":"3048_CR57","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s42979-022-01208-8","volume":"3","author":"SE Hashemi","year":"2022","unstructured":"Hashemi SE, Tavana M, Bakhshi M. A new particle swarm optimization algorithm for optimizing big data clustering. SN Comput Sci. 2022;3(4):1\u201316.","journal-title":"SN Comput Sci"},{"key":"3048_CR58","doi-asserted-by":"crossref","unstructured":"Zhu Q, Tang X, Elahi A. Automatic clustering based on dynamic parameters harmony search optimization algorithm.\u00a0Pattern Anal Appl. 2022:1\u201317.","DOI":"10.1007\/s10044-022-01065-4"},{"key":"3048_CR59","doi-asserted-by":"publisher","DOI":"10.1016\/j.cie.2022.108164","volume":"169","author":"T Kuo","year":"2022","unstructured":"Kuo T, Wang KJ. A hybrid k-prototypes clustering approach with improved sine-cosine algorithm for mixed-data classification. Comput Ind Eng. 2022;169:108164.","journal-title":"Comput Ind Eng."},{"key":"3048_CR60","doi-asserted-by":"publisher","first-page":"10541","DOI":"10.1007\/s10489-021-02934-x","volume":"52","author":"A Kaur","year":"2022","unstructured":"Kaur A, Kumar Y. Neighborhood search based improved bat algorithm for data clustering. Appl Intell. 2022;52:10541\u201375.","journal-title":"Appl Intell."},{"key":"3048_CR61","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.107682","volume":"236","author":"S Barshandeh","year":"2022","unstructured":"Barshandeh S, Dana R, Eskandarian P. A learning automata-based hybrid MPA and JS algorithm for numerical optimization problems and its application on data clustering. Knowl-Based Syst. 2022;236: 107682.","journal-title":"Knowl-Based Syst"},{"issue":"24","key":"3048_CR62","doi-asserted-by":"publisher","first-page":"13019","DOI":"10.3390\/app122413019","volume":"12","author":"AM Ikotun","year":"2022","unstructured":"Ikotun AM, Ezugwu AE. Improved SOSK-means automatic clustering algorithm with a three-part mutualism phase and random weighted reflection coefficient for high-dimensional datasets. Appl Sci. 2022;12(24):13019.","journal-title":"Appl Sci"},{"issue":"3","key":"3048_CR63","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1007\/s13748-022-00275-5","volume":"11","author":"M Mohammadi","year":"2022","unstructured":"Mohammadi M, Mobarakeh MI. An integrated clustering algorithm based on firefly algorithm and self-organized neural network. Prog Artif Intell. 2022;11(3):207\u201317.","journal-title":"Prog Artif Intell"},{"issue":"17","key":"3048_CR64","doi-asserted-by":"publisher","first-page":"24399","DOI":"10.1007\/s11042-022-12126-5","volume":"81","author":"G Suryanarayana","year":"2022","unstructured":"Suryanarayana G, Prakash KLNC, Mahesh PS, Bhaskar T. Novel dynamic k-modes clustering of categorical and non categorical dataset with optimized genetic algorithm based feature selection. Multimed Tools Appl. 2022;81(17):24399\u2013418.","journal-title":"Multimed Tools Appl"},{"issue":"9","key":"3048_CR65","doi-asserted-by":"publisher","first-page":"1563","DOI":"10.1080\/0305215X.2021.1957101","volume":"54","author":"LR De Abreu","year":"2022","unstructured":"De Abreu LR, Ara\u00fajo KAG, de Athayde Prata B, Nagano MS, Moccellin JV. A new variable neighbourhood search with a constraint programming search strategy for the open shop scheduling problem with operation repetitions. Eng Optim. 2022;54(9):1563\u201382.","journal-title":"Eng Optim"},{"issue":"8","key":"3048_CR66","doi-asserted-by":"publisher","first-page":"1475","DOI":"10.1109\/TKDE.2019.2909204","volume":"32","author":"W Li","year":"2019","unstructured":"Li W, Zhang Y, Sun Y, Wang W, Li M, Zhang W, Lin X. Approximate nearest neighbor search on high dimensional data\u2014experiments, analyses, and improvement. IEEE Trans Knowl Data Eng. 2019;32(8):1475\u201388.","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"12","key":"3048_CR67","doi-asserted-by":"publisher","first-page":"6965","DOI":"10.1007\/s00521-020-05471-9","volume":"33","author":"K Chowdhury","year":"2021","unstructured":"Chowdhury K, Chaudhuri D, Pal AK. An entropy-based initialization method of K-means clustering on the optimal number of clusters. Neural Comput Appl. 2021;33(12):6965\u201382.","journal-title":"Neural Comput Appl"},{"issue":"12","key":"3048_CR68","doi-asserted-by":"publisher","first-page":"8679","DOI":"10.1016\/j.eswa.2010.06.061","volume":"37","author":"H Jiang","year":"2010","unstructured":"Jiang H, Yi S, Li J, Yang F, Hu X. Ant clustering algorithm with K-harmonic means clustering. Expert Syst Appl. 2010;37(12):8679\u201384.","journal-title":"Expert Syst Appl"},{"key":"3048_CR69","doi-asserted-by":"publisher","first-page":"537","DOI":"10.1007\/s00521-015-2095-5","volume":"28","author":"Y Kumar","year":"2017","unstructured":"Kumar Y, Sahoo G. A two-step artificial bee colony algorithm for clustering. Neural Comput Appl. 2017;28:537\u201351.","journal-title":"Neural Comput Appl"},{"issue":"12","key":"3048_CR70","doi-asserted-by":"publisher","first-page":"1613","DOI":"10.1016\/j.patrec.2011.05.010","volume":"32","author":"W Kwedlo","year":"2011","unstructured":"Kwedlo W. A clustering method combining differential evolution with the K-means algorithm. Pattern Recogn Lett. 2011;32(12):1613\u201321.","journal-title":"Pattern Recogn Lett"},{"issue":"9","key":"3048_CR71","doi-asserted-by":"publisher","first-page":"1455","DOI":"10.1016\/S0031-3203(99)00137-5","volume":"33","author":"U Maulik","year":"2000","unstructured":"Maulik U, Bandyopadhyay S. Genetic algorithm-based clustering technique. Pattern Recogn. 2000;33(9):1455\u201365.","journal-title":"Pattern Recogn"},{"key":"3048_CR72","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1007\/s00521-016-2528-9","volume":"29","author":"K Bijari","year":"2018","unstructured":"Bijari K, Zare H, Veisi H, Bobarshad H. Memory-enriched big bang\u2013big crunch optimization algorithm for data clustering. Neural Comput Appl. 2018;29:111\u201321.","journal-title":"Neural Comput Appl"},{"issue":"4","key":"3048_CR73","doi-asserted-by":"publisher","first-page":"599","DOI":"10.1109\/LGRS.2016.2530724","volume":"13","author":"J Senthilnath","year":"2016","unstructured":"Senthilnath J, Kulkarni S, Benediktsson JA, Yang XS. A novel approach for multispectral satellite image classification based on the bat algorithm. IEEE Geosci Remote Sens Lett. 2016;13(4):599\u2013603.","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"3048_CR74","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1016\/j.ins.2014.08.053","volume":"293","author":"B Do\u011fan","year":"2015","unstructured":"Do\u011fan B, \u00d6lmez T. A new metaheuristic for numerical function optimization: vortex search algorithm. Inf Sci. 2015;293:125\u201345.","journal-title":"Inf Sci"},{"issue":"7","key":"3048_CR75","doi-asserted-by":"publisher","first-page":"1995","DOI":"10.1007\/s00521-015-1923-y","volume":"31","author":"G-G Wang","year":"2019","unstructured":"Wang G-G, Deb S, Cui Z. Monarch butterfly optimization. Neural Comput Appl. 2019;31(7):1995\u20132014.","journal-title":"Neural Comput Appl"},{"issue":"9","key":"3048_CR76","doi-asserted-by":"publisher","first-page":"2681","DOI":"10.1007\/s10489-017-1096-8","volume":"48","author":"Y Kumar","year":"2018","unstructured":"Kumar Y, Singh PK. Improved cat swarm optimization algorithm for solving global optimization problems and its application to clustering. Appl Intell. 2018;48(9):2681\u201397.","journal-title":"Appl Intell"},{"issue":"3","key":"3048_CR77","doi-asserted-by":"publisher","first-page":"1036","DOI":"10.1007\/s10489-018-1301-4","volume":"49","author":"Y Kumar","year":"2019","unstructured":"Kumar Y, Singh PK. A chaotic teaching learning based optimization algorithm for clustering problems. Appl Intell. 2019;49(3):1036\u201362.","journal-title":"Appl Intell"},{"key":"3048_CR78","doi-asserted-by":"publisher","first-page":"358","DOI":"10.1016\/j.eswa.2017.12.001","volume":"96","author":"SI Boushaki","year":"2018","unstructured":"Boushaki SI, Kamel N, Bendjeghaba O. A new quantum chaotic cuckoo search algorithm for data clustering. Expert Syst Appl. 2018;96:358\u201372.","journal-title":"Expert Syst Appl"},{"key":"3048_CR79","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.engappai.2016.11.003","volume":"61","author":"X Han","year":"2017","unstructured":"Han X, Quan L, Xiong X, Almeter M, Xiang J, Lan Y. A novel data clustering algorithm based on modified gravitational search algorithm. Eng Appl Artif Intell. 2017;61:1\u20137.","journal-title":"Eng Appl Artif Intell"},{"issue":"1","key":"3048_CR80","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1016\/j.aej.2016.12.013","volume":"57","author":"S Chander","year":"2018","unstructured":"Chander S, Vijaya P, Dhyani P. Multi kernel and dynamic fractional lion optimization algorithm for data clustering. Alex Eng J. 2018;57(1):267\u201376.","journal-title":"Alex Eng J"},{"issue":"1","key":"3048_CR81","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1109\/4235.585893","volume":"1","author":"DH Wolpert","year":"1997","unstructured":"Wolpert DH, Macready WG. No free lunch theorems for optimization. IEEE Trans Evol Comput. 1997;1(1):67\u201382.","journal-title":"IEEE Trans Evol Comput"},{"key":"3048_CR82","doi-asserted-by":"publisher","DOI":"10.1007\/s11227-023-05822-y","author":"RC Sahoo","year":"2023","unstructured":"Sahoo RC, Kumar T, Tanwar P, et al. An efficient meta-heuristic algorithm based on water flow optimizer for data clustering. J Supercomput. 2023. https:\/\/doi.org\/10.1007\/s11227-023-05822-y.","journal-title":"J Supercomput"}],"container-title":["SN Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-024-03048-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s42979-024-03048-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-024-03048-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,17]],"date-time":"2024-07-17T21:06:15Z","timestamp":1721250375000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s42979-024-03048-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,17]]},"references-count":82,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2024,8]]}},"alternative-id":["3048"],"URL":"https:\/\/doi.org\/10.1007\/s42979-024-03048-0","relation":{},"ISSN":["2661-8907"],"issn-type":[{"type":"electronic","value":"2661-8907"}],"subject":[],"published":{"date-parts":[[2024,7,17]]},"assertion":[{"value":"5 October 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 June 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 July 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"There is no competing of interests among authors.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Either human or animal are not involved in this research work.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Research involving human and\/or animals"}},{"value":"The data is publically available at UCI repository and can be available free of cost.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical and informed consent for data used"}}],"article-number":"715"}}