{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T14:12:28Z","timestamp":1760710348416,"version":"3.37.3"},"reference-count":48,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2020,8,25]],"date-time":"2020-08-25T00:00:00Z","timestamp":1598313600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,8,25]],"date-time":"2020-08-25T00:00:00Z","timestamp":1598313600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100003329","name":"Ministerio de Econom\u00eda y Competitividad","doi-asserted-by":"publisher","award":["TRA2016-76914-C3-2-P"],"award-info":[{"award-number":["TRA2016-76914-C3-2-P"]}],"id":[{"id":"10.13039\/501100003329","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003176","name":"Ministerio de Educaci\u00f3n, Cultura y Deporte","doi-asserted-by":"publisher","award":["FPU16\/00792"],"award-info":[{"award-number":["FPU16\/00792"]}],"id":[{"id":"10.13039\/501100003176","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Soft Comput"],"published-print":{"date-parts":[[2021,1]]},"DOI":"10.1007\/s00500-020-05244-5","type":"journal-article","created":{"date-parts":[[2020,8,25]],"date-time":"2020-08-25T19:03:56Z","timestamp":1598382236000},"page":"1543-1561","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["A methodology for automatic parameter-tuning and center selection in density-peak clustering methods"],"prefix":"10.1007","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7065-0728","authenticated-orcid":false,"given":"Jos\u00e9 Carlos","family":"Garc\u00eda-Garc\u00eda","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ricardo","family":"Garc\u00eda-R\u00f3denas","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,8,25]]},"reference":[{"key":"5244_CR1","doi-asserted-by":"publisher","first-page":"375","DOI":"10.1016\/j.patcog.2017.06.023","volume":"71","author":"L Bai","year":"2017","unstructured":"Bai L, Cheng X, Liang J, Shen H, Guo Y (2017) Fast density clustering strategies based on the $$k-$$means algorithm. Pattern Recognit 71:375\u2013386","journal-title":"Pattern Recognit"},{"issue":"5","key":"5244_CR2","doi-asserted-by":"publisher","first-page":"785","DOI":"10.1007\/s00779-016-0954-4","volume":"20","author":"R Bie","year":"2016","unstructured":"Bie R, Mehmood R, Ruan S, Sun Y, Dawood H (2016) Adaptive fuzzy clustering by fast search and find of density peaks. Pers Ubiquit Comput 20(5):785\u2013793","journal-title":"Pers Ubiquit Comput"},{"issue":"4356127","key":"5244_CR3","first-page":"1","volume":"2016","author":"F Bu","year":"2016","unstructured":"Bu F, Chen Z, Li P, Tang T, Zhang Y (2016) A high-order CFS algorithm for clustering big data. Mob Inf Syst 2016(4356127):1\u20138","journal-title":"Mob Inf Syst"},{"key":"5244_CR4","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1016\/j.knosys.2015.09.025","volume":"90","author":"G Chen","year":"2015","unstructured":"Chen G, Zhang X, Wang Z, Li F (2015) Robust support vector data description for outlier detection with noise or uncertain data. Knowl-Based Syst 90:129\u2013137","journal-title":"Knowl-Based Syst"},{"issue":"10","key":"5244_CR5","first-page":"1798","volume":"41","author":"J-Y Chen","year":"2015","unstructured":"Chen J-Y, He H-H (2015) Research on density-based clustering algorithm for mixed data with determine cluster centers automatically. Acta Autom Sin 41(10):1798\u20131813","journal-title":"Acta Autom Sin"},{"key":"5244_CR6","doi-asserted-by":"publisher","first-page":"271","DOI":"10.1016\/j.ins.2016.01.071","volume":"345","author":"J-Y Chen","year":"2016","unstructured":"Chen J-Y, He H-H (2016) A fast density-based data stream clustering algorithm with cluster centers self-determined for mixed data. Inf Sci 345:271\u2013293","journal-title":"Inf Sci"},{"key":"5244_CR7","doi-asserted-by":"publisher","first-page":"486","DOI":"10.1016\/j.patcog.2016.04.018","volume":"60","author":"M Chen","year":"2016","unstructured":"Chen M, Li L, Wang B, Cheng J, Pan L, Chen X (2016) Effectively clustering by finding density backbone based-on kNN. Pattern Recognit 60:486\u2013498","journal-title":"Pattern Recognit"},{"key":"5244_CR8","unstructured":"Criminisi A, Shotton J, Konukoglu E (2011) Decision forests for classification, regression, density estimation, manifold. Microsoft Research technical report"},{"key":"5244_CR9","unstructured":"Dheeru D, Karra Taniskidou E (2017) UCI machine learning repository. http:\/\/archive.ics.uci.edu\/ml"},{"key":"5244_CR10","doi-asserted-by":"crossref","unstructured":"Ding J, Chen Z, He X, Zhan Y (2016) Clustering by finding density peaks based on Chebyshev\u2019s inequality. In: Chinese control conference, CCC, pp 7169\u20137172","DOI":"10.1109\/ChiCC.2016.7554490"},{"issue":"9","key":"5244_CR11","doi-asserted-by":"publisher","first-page":"2777","DOI":"10.1007\/s00500-017-2748-7","volume":"22","author":"J Ding","year":"2018","unstructured":"Ding J, He X, Yuan J, Jiang B (2018) Automatic clustering based on density peak detection using generalized extreme value distribution. Soft Comput 22(9):2777\u20132796","journal-title":"Soft Comput"},{"key":"5244_CR12","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1016\/j.knosys.2016.02.001","volume":"99","author":"M Du","year":"2016","unstructured":"Du M, Ding S, Jia H (2016) Study on density peaks clustering based on $$k-$$nearest neighbors and principal component analysis. Knowl-Based Syst 99:135\u2013145","journal-title":"Knowl-Based Syst"},{"key":"5244_CR13","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1016\/j.patrec.2017.07.001","volume":"97","author":"M Du","year":"2017","unstructured":"Du M, Ding S, Xue Y (2017) A novel density peaks clustering algorithm for mixed data. Pattern Recognit Lett 97:46\u201353","journal-title":"Pattern Recognit Lett"},{"key":"5244_CR14","doi-asserted-by":"crossref","unstructured":"Gao J, Zhao L, Chen Z, Li P, Xu H, Hu Y (2016) ICFS: an improved fast search and find of density peaks clustering algorithm. In: Proceedings\u20142016 IEEE 14th international conference on dependable, autonomic and secure computing, DASC 2016, 2016 IEEE 14th international conference on pervasive intelligence and computing, PICom 2016, 2016 IEEE 2nd international conference on big data intelligence and computing, DataCom 2016 and 2016 IEEE Cyber Science and Technology Congress, CyberSciTech 2016, DASC-PICom-DataCom-CyberSciTech 2016, pp 537\u2013543","DOI":"10.1109\/DASC-PICom-DataCom-CyberSciTec.2016.103"},{"issue":"6","key":"5244_CR15","first-page":"1400","volume":"53","author":"S Gong","year":"2016","unstructured":"Gong S, Zhang Y (2016) EDDPC: an efficient distributed density peaks clustering algorithm. Comput Res Dev 53(6):1400\u20131409","journal-title":"Comput Res Dev"},{"key":"5244_CR16","unstructured":"Guo P, Xing W, Yubing W, Yue C, Ying Z (2017) Research on automatic determining clustering centers algorithm based on linear regression analysis. In: 2nd International conference on image, vision and computing, pp 1016\u20131023"},{"issue":"8","key":"5244_CR17","doi-asserted-by":"publisher","first-page":"1547","DOI":"10.1109\/TPAMI.2016.2609929","volume":"39","author":"DP Hofmeyr","year":"2017","unstructured":"Hofmeyr DP (2017) Clustering by minimum cut hyperplanes. IEEE Trans Pattern Anal Mach Intell 39(8):1547\u20131560","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"10","key":"5244_CR18","doi-asserted-by":"publisher","first-page":"6935","DOI":"10.1166\/jctn.2016.5650","volume":"13","author":"J-L Hua","year":"2016","unstructured":"Hua J-L, Yu J, Yang M-S (2016) Correlative density-based clustering. J Comput Theor Nanosci 13(10):6935\u20136943","journal-title":"J Comput Theor Nanosci"},{"key":"5244_CR19","doi-asserted-by":"publisher","first-page":"345","DOI":"10.1016\/j.physa.2018.02.084","volume":"502","author":"J Jiang","year":"2018","unstructured":"Jiang J, Hao D, Chen Y, Parmar M, Li K (2018) GDPC: gravitation-based density peaks clustering algorithm. Physica A 502:345\u2013355","journal-title":"Physica A"},{"key":"5244_CR20","doi-asserted-by":"publisher","first-page":"539","DOI":"10.1016\/j.asoc.2017.04.031","volume":"57","author":"C Jinyin","year":"2017","unstructured":"Jinyin C, Xiang L, Haibing Z, Xintong B (2017) A novel cluster center fast determination clustering algorithm. Appl Soft Comput J 57:539\u2013555","journal-title":"Appl Soft Comput J"},{"key":"5244_CR21","doi-asserted-by":"crossref","unstructured":"Kun D, Ze W, Rui Z, Chao Y (2016) Clustering by exponential density analysis and find of cluster centers based on genetic algorithm. In: Proceedings of SPIE\u2014the international society for optical engineering (ICDIP 2016), vol 10033","DOI":"10.1117\/12.2244868"},{"key":"5244_CR22","unstructured":"Lee K (2005) Yale face database B. http:\/\/vision.ucsd.edu\/~leekc\/ExtYaleDatabase\/l"},{"issue":"3","key":"5244_CR23","doi-asserted-by":"crossref","first-page":"173","DOI":"10.2991\/ijndc.2016.4.3.4","volume":"4","author":"M Li","year":"2016","unstructured":"Li M, Huang J, Wang J (2016) Paralleled fast search and find of density peaks clustering algorithm on gpus with cuda. Int J Netw Distrib Comput 4(3):173\u2013181","journal-title":"Int J Netw Distrib Comput"},{"key":"5244_CR24","doi-asserted-by":"publisher","first-page":"236","DOI":"10.1016\/j.eswa.2017.11.020","volume":"95","author":"Z Li","year":"2018","unstructured":"Li Z, Tang Y (2018) Comparative density peaks clustering. Expert Syst Appl 95:236\u2013247","journal-title":"Expert Syst Appl"},{"key":"5244_CR25","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1016\/j.patrec.2016.01.009","volume":"73","author":"Z Liang","year":"2016","unstructured":"Liang Z, Chen P (2016) Delta-density based clustering with a divide-and-conquer strategy: 3DC clustering. Pattern Recognit Lett 73:52\u201359","journal-title":"Pattern Recognit Lett"},{"key":"5244_CR26","doi-asserted-by":"publisher","first-page":"200","DOI":"10.1016\/j.ins.2018.03.031","volume":"450","author":"R Liu","year":"2018","unstructured":"Liu R, Wang H, Yu X (2018) Shared-nearest-neighbor-based clustering by fast search and find of density peaks. Inf Sci 450:200\u2013226","journal-title":"Inf Sci"},{"issue":"5060842","key":"5244_CR27","first-page":"1","volume":"2017","author":"S Liu","year":"2017","unstructured":"Liu S, Zhou B, Huang D, Shen L (2017) Clustering mixed data by fast search and find of density peaks. Math Probl Eng 2017(5060842):1\u20137","journal-title":"Math Probl Eng"},{"key":"5244_CR28","doi-asserted-by":"crossref","unstructured":"Liu Y, Li Z, Xiong H, Gao X, Wu J (2010) Understanding of internal clustering validation measures. In: Proceedings of the 2010 IEEE international conference on data mining, ICDM \u201910, pp 911\u2013916. IEEE Computer Society, Washington","DOI":"10.1109\/ICDM.2010.35"},{"key":"5244_CR29","doi-asserted-by":"publisher","first-page":"231","DOI":"10.1016\/j.neucom.2014.09.048","volume":"151","author":"ML L\u00f3pez-Garc\u00eda","year":"2015","unstructured":"L\u00f3pez-Garc\u00eda ML, Garc\u00eda-R\u00f3denas R, G\u00f3mez AG (2015) K-means algorithms for functional data. Neurocomputing 151:231\u2013245","journal-title":"Neurocomputing"},{"key":"5244_CR30","doi-asserted-by":"publisher","first-page":"4991","DOI":"10.1109\/ACCESS.2017.2688477","volume":"5","author":"J Lu","year":"2017","unstructured":"Lu J, Zhu Q (2017) An effective algorithm based on density clustering framework. IEEE Access 5:4991\u20135000","journal-title":"IEEE Access"},{"issue":"5","key":"5244_CR31","doi-asserted-by":"publisher","first-page":"2619","DOI":"10.3233\/JIFS-169102","volume":"31","author":"R Mehmood","year":"2016","unstructured":"Mehmood R, Bie R, Jiao L, Dawood H, Sun Y (2016a) Adaptive cutoff distance: clustering by fast search and find of density peaks. J Intell Fuzzy Sys 31(5):2619\u20132628","journal-title":"J Intell Fuzzy Sys"},{"key":"5244_CR32","doi-asserted-by":"publisher","first-page":"210","DOI":"10.1016\/j.neucom.2016.01.102","volume":"208","author":"R Mehmood","year":"2016","unstructured":"Mehmood R, Zhang G, Bie R, Dawood H, Ahmad H (2016b) Clustering by fast search and find of density peaks via heat diffusion. Neurocomputing 208:210\u2013217","journal-title":"Neurocomputing"},{"issue":"6191","key":"5244_CR33","doi-asserted-by":"publisher","first-page":"1492","DOI":"10.1126\/science.1242072","volume":"344","author":"A Rodr\u00edguez","year":"2014","unstructured":"Rodr\u00edguez A, Laio A (2014) Clustering by fast search and find of density peaks. Science 344(6191):1492\u20131496","journal-title":"Science"},{"key":"5244_CR34","unstructured":"Rosenberg A, Hirschberg J (2007) V-measure: a conditional entropy-based external cluster evaluation measure. In: Proceedings of the 2007 joint conference on empirical methods in natural language processing and computational natural language learning, vol 7, pp 410\u2013420"},{"issue":"9","key":"5244_CR35","doi-asserted-by":"publisher","first-page":"3046","DOI":"10.1016\/j.patcog.2014.03.006","volume":"47","author":"J Tabor","year":"2014","unstructured":"Tabor J, Spurek P (2014) Cross-entropy clustering. Pattern Recognit 47(9):3046\u20133059","journal-title":"Pattern Recognit"},{"key":"5244_CR36","doi-asserted-by":"crossref","unstructured":"Tao L, Li W, Jin Y (2017) An optimal density peak algorithm based on data field and information entropy. In: ACM international conference proceeding series, vol Part F128770","DOI":"10.1145\/3089871.3089880"},{"issue":"8","key":"5244_CR37","doi-asserted-by":"publisher","first-page":"1971","DOI":"10.1109\/TKDE.2016.2535209","volume":"28","author":"G Wang","year":"2016","unstructured":"Wang G, Song Q (2016) Automatic clustering via outward statistical testing on density metrics. IEEE Trans Knowl Data Eng 28(8):1971\u20131985","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"5244_CR38","doi-asserted-by":"publisher","first-page":"1718","DOI":"10.1109\/ACCESS.2017.2780109","volume":"6","author":"J Wang","year":"2017","unstructured":"Wang J, Zhu C, Zhou Y, Zhu X, Wang Y, Zhang W (2017) From partition-based clustering to density-based clustering: fast find clusters with diverse shapes and densities in spatial databases. IEEE Access 6:1718\u20131729","journal-title":"IEEE Access"},{"key":"5244_CR39","doi-asserted-by":"publisher","first-page":"219","DOI":"10.1016\/j.neucom.2015.11.091","volume":"179","author":"M Wang","year":"2016","unstructured":"Wang M, Zuo W, Wang Y (2016) An improved density peaks-based clustering method for social circle discovery in social networks. Neurocomputing 179:219\u2013227","journal-title":"Neurocomputing"},{"issue":"6","key":"5244_CR40","doi-asserted-by":"publisher","first-page":"2800","DOI":"10.1177\/0962280215609948","volume":"26","author":"X-F Wang","year":"2017","unstructured":"Wang X-F, Xu Y (2017) Fast clustering using adaptive density peak detection. Stat Methods Med Res 26(6):2800\u20132811","journal-title":"Stat Methods Med Res"},{"issue":"11","key":"5244_CR41","doi-asserted-by":"publisher","first-page":"1033","DOI":"10.1038\/nmeth.3583","volume":"12","author":"C Wiwie","year":"2015","unstructured":"Wiwie C, Baumbach J, R\u00f6ttger R (2015) Comparing the performance of biomedical clustering methods. Nat Methods 12(11):1033\u20131038","journal-title":"Nat Methods"},{"key":"5244_CR42","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1016\/j.ins.2016.03.011","volume":"354","author":"J Xie","year":"2016","unstructured":"Xie J, Gao H, Xie W, Liu X, Grant P (2016) Robust clustering by detecting density peaks and assigning points based on fuzzy weighted $$k-$$nearest neighbors. Inf Sci 354:19\u201340","journal-title":"Inf Sci"},{"key":"5244_CR43","doi-asserted-by":"publisher","first-page":"200","DOI":"10.1016\/j.ins.2016.08.086","volume":"373","author":"J Xu","year":"2016","unstructured":"Xu J, Wang G, Deng W (2016) DenPEHC: density peak based efficient hierarchical clustering. Inf Sci 373:200\u2013218","journal-title":"Inf Sci"},{"issue":"13","key":"5244_CR44","doi-asserted-by":"publisher","first-page":"5171","DOI":"10.1007\/s00500-018-3183-0","volume":"23","author":"X Xu","year":"2019","unstructured":"Xu X, Ding S, Xu H, Liao H, Xue Y (2019) A feasible density peaks clustering algorithm with a merging strategy. Soft Comput 23(13):5171\u20135183","journal-title":"Soft Comput"},{"key":"5244_CR45","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1016\/j.patrec.2017.10.025","volume":"100","author":"X-H Yang","year":"2017","unstructured":"Yang X-H, Zhu Q-P, Huang Y-J, Xiao J, Wang L, Tong F-C (2017) Parameter-free laplacian centrality peaks clustering. Pattern Recognit Lett 100:167\u2013173","journal-title":"Pattern Recognit Lett"},{"key":"5244_CR46","doi-asserted-by":"publisher","first-page":"208","DOI":"10.1016\/j.knosys.2017.07.010","volume":"133","author":"L Yaohui","year":"2017","unstructured":"Yaohui L, Zhengming M, Fang Y (2017) Adaptive density peak clustering based on $$k$$-nearest neighbors with aggregating strategy. Knowl-Based Syst 133:208\u2013220","journal-title":"Knowl-Based Syst"},{"key":"5244_CR47","doi-asserted-by":"crossref","unstructured":"Zang W, Ren L, Zhang W, Liu X (2017) Automatic density peaks clustering using DNA genetic algorithm optimized data field and Gaussian process. Int J Pattern Recognit Artif Intell 31(8)","DOI":"10.1142\/S0218001417500239"},{"key":"5244_CR48","unstructured":"Zhao Y, Karypis G (2001) Criterion functions for document clustering: experiments and analysis. Tech. Rep., pp 01\u201304"}],"container-title":["Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-020-05244-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00500-020-05244-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-020-05244-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,6]],"date-time":"2023-10-06T08:32:45Z","timestamp":1696581165000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00500-020-05244-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,8,25]]},"references-count":48,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2021,1]]}},"alternative-id":["5244"],"URL":"https:\/\/doi.org\/10.1007\/s00500-020-05244-5","relation":{},"ISSN":["1432-7643","1433-7479"],"issn-type":[{"type":"print","value":"1432-7643"},{"type":"electronic","value":"1433-7479"}],"subject":[],"published":{"date-parts":[[2020,8,25]]},"assertion":[{"value":"25 August 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Informed consent was obtained from all individual participants included in the study.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}}]}}