{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T08:32:10Z","timestamp":1758270730533,"version":"3.37.3"},"reference-count":35,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2022,2,15]],"date-time":"2022-02-15T00:00:00Z","timestamp":1644883200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,2,15]],"date-time":"2022-02-15T00:00:00Z","timestamp":1644883200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61877065"],"award-info":[{"award-number":["61877065"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Cogn Comput"],"published-print":{"date-parts":[[2022,7]]},"DOI":"10.1007\/s12559-022-10002-w","type":"journal-article","created":{"date-parts":[[2022,2,15]],"date-time":"2022-02-15T12:24:29Z","timestamp":1644927869000},"page":"1350-1361","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["A Novel Fuzzy Distance-Based Minimum Spanning Tree Clustering Algorithm for Face Detection"],"prefix":"10.1007","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4772-3607","authenticated-orcid":false,"given":"Yang","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenju","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,2,15]]},"reference":[{"key":"10002_CR1","doi-asserted-by":"publisher","first-page":"172","DOI":"10.1016\/j.neucom.2015.10.140","volume":"211","author":"J Fan","year":"2016","unstructured":"Fan J, Niu Z, Liang Y, et al. Probability model selection and parameter evolutionary estimation for clustering imbalanced data without sampling. Neurocomputing. 2016;211:172\u201381.","journal-title":"Neurocomputing"},{"key":"10002_CR2","unstructured":"Moore A W. Very fast EM-based mixture model clustering using multiresolution Kd-trees. Neural Information Processing systems.\u00a02020; 543\u2013549."},{"issue":"1","key":"10002_CR3","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1109\/T-C.1971.223083","volume":"20","author":"CT Zahn","year":"2019","unstructured":"Zahn CT. Graph-theoretical methods for detecting and describing gestalt clusters. IEEE Trans Comput. 2019;20(1):68\u201386.","journal-title":"IEEE Trans Comput"},{"key":"10002_CR4","doi-asserted-by":"crossref","unstructured":"Doucet, Arnaud, Freitas, Nando, Gordon, Neil. Sequential Monte Carlo methods in practice || an introduction to sequential monte carlo methods. 2020, 1(1):3\u201314.","DOI":"10.1007\/978-1-4757-3437-9_1"},{"issue":"3","key":"10002_CR5","doi-asserted-by":"publisher","first-page":"752","DOI":"10.1016\/j.patcog.2009.07.010","volume":"43","author":"C Zhong","year":"2010","unstructured":"Zhong C, Miao D, Wang R. A graph-theoretical clustering method based on two rounds of minimum spanning trees. Pattern Recogn. 2010;43(3):752\u201366.","journal-title":"Pattern Recogn."},{"issue":"2","key":"10002_CR6","doi-asserted-by":"publisher","first-page":"180","DOI":"10.1109\/34.908968","volume":"23","author":"MB Rhouma","year":"2001","unstructured":"Rhouma MB, Frigui H. Self-organization of pulse-coupled oscillators with application to clustering. IEEE Trans Pattern Anal Mach Intell. 2001;23(2):180\u201395.","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"10002_CR7","unstructured":"Han J. Data mining: concepts and techniques. 2005."},{"issue":"3","key":"10002_CR8","first-page":"486","volume":"40","author":"WR Fox","year":"2020","unstructured":"Fox WR. Finding groups in data: an introduction to cluster analysis. J R Stat Soc: Ser C: Appl Stat. 2020;40(3):486\u20137.","journal-title":"J R Stat Soc: Ser C: Appl Stat"},{"issue":"5","key":"10002_CR9","doi-asserted-by":"publisher","first-page":"1003","DOI":"10.1109\/TKDE.2002.1033770","volume":"14","author":"RT Ng","year":"2018","unstructured":"Ng RT, Han J. CLARANS: a method for clustering objects for spatial data mining. IEEE Trans Knowl Data Eng. 2018;14(5):1003\u201316.","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"10002_CR10","unstructured":"Hinneburg A. An efficient approach to clustering in large multimedia databases with noise. Knowledge Discovery and Data Mining (KDD '98), 1998; 13(4):332\u2013344."},{"key":"10002_CR11","doi-asserted-by":"crossref","unstructured":"Ankerst M, Breunig M M, Kriegel H P, et al. OPTICS: ordering points to identify the clustering structure. SIGMOD 1999, Proceedings ACM SIGMOD International Conference on Management of Data, June 1\u20133, 1999, Philadelphia, Pennsylvania, USA. ACM.\u00a01999.","DOI":"10.1145\/304182.304187"},{"issue":"2","key":"10002_CR12","first-page":"103","volume":"25","author":"T Zhang","year":"1996","unstructured":"Zhang T, Ramakrishnan R, Livny M, et al. BIRCH: an efficient data clustering method for very large databases. International Conference on Management of Data. 1996;25(2):103\u201314.","journal-title":"International Conference on Management of Data"},{"issue":"1","key":"10002_CR13","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1016\/S0306-4379(01)00008-4","volume":"26","author":"S Guha","year":"2001","unstructured":"Guha S, Rastogi R, Shim K, et al. Cure: an efficient clustering algorithm for large databases. Inf Syst. 2001;26(1):35\u201358.","journal-title":"Inf Syst"},{"key":"10002_CR14","doi-asserted-by":"publisher","first-page":"80716","DOI":"10.1109\/ACCESS.2020.2988796","volume":"8","author":"KP Sinaga","year":"2020","unstructured":"Sinaga KP, Yang M. Unsupervised K-means clustering algorithm. IEEE Access. 2020;8:80716\u201327.","journal-title":"IEEE Access"},{"issue":"2","key":"10002_CR15","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1109\/TIT.1982.1056489","volume":"28","author":"SP Lloyd","year":"1982","unstructured":"Lloyd SP. Least squares quantization in PCM. IEEE Trans Inf Theory. 1982;28(2):129\u201337.","journal-title":"IEEE Trans Inf Theory"},{"key":"10002_CR16","doi-asserted-by":"publisher","first-page":"875","DOI":"10.1007\/s12559-018-9591-8","volume":"10","author":"R Li","year":"2018","unstructured":"Li R, Wang S, Gu D. Ongoing evolution of visual SLAM from geometry to deep learning: challenges and opportunities. Cogn Comput. 2018;10:875\u201389.","journal-title":"Cogn Comput"},{"key":"10002_CR17","doi-asserted-by":"crossref","unstructured":"Xie J, Jiang W, Ding L. Clustering by searching density peaks via local standard deviation. International Conference on Intelligent Data Engineering and Automated Learning. Springer, Cham, 2017; 4:295\u2013305.","DOI":"10.1007\/978-3-319-68935-7_33"},{"issue":"4","key":"10002_CR18","first-page":"112","volume":"10","author":"Xu Yang","year":"2020","unstructured":"Yang Xu, Deng C, Wei K, Yan J, Liu W. Adversarial learning for robust deep clustering. NeurIPS Proceedings. 2020;10(4):112\u20139.","journal-title":"NeurIPS Proceedings"},{"key":"10002_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.neucom.2018.05.092","volume":"319","author":"WeipingHuang RuhuiLiu","year":"2018","unstructured":"RuhuiLiu WeipingHuang, ZhengshunFei KaiWang, JunLiang,. Constraint-based clustering by fast search and find of density peaks. Neurocomputing. 2018;319:1\u2013196.","journal-title":"Neurocomputing"},{"key":"10002_CR20","doi-asserted-by":"crossref","unstructured":"Ankerst M, Breunig MM, Kriegel HP, et al. OPTICS: ordering points to identify the clustering structure. Proceeding of the ACM SIGMOD International Conference on Management of Data. Philadelphia.\u00a02020; 6:49\u201360.","DOI":"10.1145\/304181.304187"},{"issue":"5814","key":"10002_CR21","doi-asserted-by":"publisher","first-page":"972","DOI":"10.1126\/science.1136800","volume":"315","author":"BJ Frey","year":"2007","unstructured":"Frey BJ, Eueck D. Clustering by passing messages between data points. Science. 2007;315(5814):972\u20136.","journal-title":"Science"},{"issue":"5","key":"10002_CR22","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, et al. Adaptive fuzzy clustering by fast search and find of density peaks. Pers Ubiquit Comput. 2016;20(5):785\u201393.","journal-title":"Pers Ubiquit Comput"},{"issue":"6191","key":"10002_CR23","doi-asserted-by":"publisher","first-page":"1492","DOI":"10.1126\/science.1242072","volume":"344","author":"A Rodriguez","year":"2020","unstructured":"Rodriguez A, Laio A. Clustering by fast search and find of density peaks. Science. 2020;344(6191):1492\u20136.","journal-title":"Science"},{"key":"10002_CR24","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1016\/j.ins.2016.03.011","volume":"354","author":"J Xie","year":"2019","unstructured":"Xie J, Gao H, Xie W, et al. Robust clustering by detecting density peaks and assigning points based on fuzzy weighted K-nearest neighbors. Inf Sci. 2019;354:19\u201340.","journal-title":"Inf Sci"},{"key":"10002_CR25","doi-asserted-by":"crossref","unstructured":"Xie JY, Gao HC, Xie WX. K-nearest neighbors optimized clustering algorithm by fast search and finding the density peaks of a dataset. Science Chia: Inform Sci.\u00a02016; 46(2):258\u2013280.","DOI":"10.1360\/N112015-00135"},{"key":"10002_CR26","unstructured":"Weihua H, Takeru M, Seiya T, Eiichi M, Masashi S. Learning discrete representations via information maximizing self-augmented training. In Proceedings of the 34th International Conference on Machine Learning (ICML'17). 2017; 1558\u20131567."},{"key":"10002_CR27","doi-asserted-by":"publisher","first-page":"5880","DOI":"10.1109\/ICCV.2017.626","volume":"2017","author":"J Chang","year":"2017","unstructured":"Chang J, Wang L, Meng G, Xiang S, Pan C. Deep adaptive image clustering. IEEE International Conference on Computer Vision (ICCV). 2017;2017:5880\u20138.","journal-title":"IEEE International Conference on Computer Vision (ICCV)"},{"key":"10002_CR28","doi-asserted-by":"publisher","first-page":"98","DOI":"10.3390\/computers9040098","volume":"9","author":"N Kumar","year":"2020","unstructured":"Kumar N, Gumhold S. FuseVis: interpreting neural networks for image fusion using per-pixel saliency visualization. Computers. 2020;9:98.","journal-title":"Computers"},{"issue":"3","key":"10002_CR29","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1109\/TETC.2014.2330519","volume":"2","author":"A Fahad","year":"2014","unstructured":"Fahad A, Alshatri N, Tari Z, et al. A survey of clustering algorithms for big data: taxonomy and empirical analysis. IEEE Trans Emerg Top Comput. 2014;2(3):267\u201379.","journal-title":"IEEE Trans Emerg Top Comput"},{"issue":"4","key":"10002_CR30","doi-asserted-by":"publisher","first-page":"536","DOI":"10.1093\/bioinformatics\/18.4.536","volume":"18","author":"Y Xu","year":"2020","unstructured":"Xu Y, Olman V, Xu D, et al. Clustering gene expression data using a graph-theoretic approach: an application of minimum spanning trees. Bioinformatics. 2020;18(4):536\u201345.","journal-title":"Bioinformatics"},{"issue":"5","key":"10002_CR31","doi-asserted-by":"publisher","first-page":"1523","DOI":"10.1016\/j.dsp.2013.03.009","volume":"23","author":"X Wang","year":"2019","unstructured":"Wang X, Wang XL, Chen C, et al. Enhancing minimum spanning tree-based clustering by removing density-based outliers. Digital Signal Process. 2019;23(5):1523\u201338.","journal-title":"Digital Signal Process"},{"key":"10002_CR32","doi-asserted-by":"publisher","first-page":"702","DOI":"10.1016\/j.physa.2019.03.012","volume":"523","author":"J Jiang","year":"2019","unstructured":"Jiang J, Chen Y, Meng X, et al. A novel density peaks clustering algorithm based on k nearest neighbors for improving assignment process. Physica A. 2019;523:702\u201313.","journal-title":"Physica A"},{"issue":"99","key":"10002_CR33","first-page":"1","volume":"6","author":"MD Parmar","year":"2019","unstructured":"Parmar MD, Pang W, Hao D, et al. FREDPC: a feasible residual error-based density peak clustering algorithm with the fragment merging strategy. IEEE Access. 2019;6(99):1\u20137.","journal-title":"IEEE Access"},{"issue":"6191","key":"10002_CR34","doi-asserted-by":"publisher","first-page":"1492","DOI":"10.1126\/science.1242072","volume":"344","author":"A Rodriguez","year":"2014","unstructured":"Rodriguez A, Laio A. Clustering by fast search and find of density peaks. Science. 2014;344(6191):1492\u20136.","journal-title":"Science"},{"key":"10002_CR35","doi-asserted-by":"crossref","unstructured":"Yan H, Wang L, Lu Y. Identifying cluster centroids from decision graph automatically using a statistical outlier detection method. Neurocomputing.\u00a02019; 329(FEB.15):348\u2013358.","DOI":"10.1016\/j.neucom.2018.10.067"}],"container-title":["Cognitive Computation"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12559-022-10002-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12559-022-10002-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12559-022-10002-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,22]],"date-time":"2022-07-22T13:20:49Z","timestamp":1658496049000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12559-022-10002-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,15]]},"references-count":35,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2022,7]]}},"alternative-id":["10002"],"URL":"https:\/\/doi.org\/10.1007\/s12559-022-10002-w","relation":{},"ISSN":["1866-9956","1866-9964"],"issn-type":[{"type":"print","value":"1866-9956"},{"type":"electronic","value":"1866-9964"}],"subject":[],"published":{"date-parts":[[2022,2,15]]},"assertion":[{"value":"15 December 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 January 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 February 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This article does not contain any experiments with human or animal participants performed by any of the authors.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics Approval"}},{"value":"Informed consent was obtained from all individual participants included in the study.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to Participate"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}}]}}