{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T20:04:52Z","timestamp":1781294692865,"version":"3.54.1"},"reference-count":28,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2018,6,29]],"date-time":"2018-06-29T00:00:00Z","timestamp":1530230400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61272194"],"award-info":[{"award-number":["61272194"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61702060"],"award-info":[{"award-number":["61702060"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. & Cyber."],"published-print":{"date-parts":[[2019,7]]},"DOI":"10.1007\/s13042-018-0836-3","type":"journal-article","created":{"date-parts":[[2018,6,29]],"date-time":"2018-06-29T04:02:16Z","timestamp":1530244936000},"page":"1591-1602","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["A hierarchical clustering algorithm based on noise removal"],"prefix":"10.1007","volume":"10","author":[{"given":"Dongdong","family":"Cheng","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qingsheng","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinlong","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Quanwang","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lijun","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2018,6,29]]},"reference":[{"issue":"2","key":"836_CR1","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1145\/335191.335388","volume":"29","author":"MM Breunig","year":"2000","unstructured":"Breunig MM, Kriegel HP, Ng RT, Sander J (2000) Lof: identifying density-based local outliers. Acm Sigmod Record 29(2):93\u2013104","journal-title":"Acm Sigmod Record"},{"issue":"3","key":"836_CR2","doi-asserted-by":"publisher","first-page":"568","DOI":"10.1109\/TPAMI.2010.88","volume":"33","author":"WY Chen","year":"2011","unstructured":"Chen WY, Song Y, Bai H, Lin CJ, Chang EY (2011) Parallel spectral clustering in distributed systems. IEEE Trans Pattern Anal Mach Intell 33(3):568\u2013586","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"836_CR3","doi-asserted-by":"publisher","first-page":"238","DOI":"10.1016\/j.knosys.2017.02.027","volume":"123C","author":"D Cheng","year":"2017","unstructured":"Cheng D, Zhu Q, Huang J, Yang L, Wu Q (2017) Natural neighbor-based clustering algorithm with local representatives. Knowl Based Syst 123C:238\u2013253","journal-title":"Knowl Based Syst"},{"key":"836_CR4","unstructured":"Ester M, Kriegel HP, Xu X (1996) A density-based algorithm for discovering clusters a density-based algorithm for discovering clusters in large spatial databases with noise. In: International Conference on Knowledge Discovery and Data Mining, pp 226\u2013231"},{"issue":"5814","key":"836_CR5","doi-asserted-by":"publisher","first-page":"972","DOI":"10.1126\/science.1136800","volume":"315","author":"BJ Frey","year":"2007","unstructured":"Frey BJ, Dueck D (2007) Clustering by passing messages between data points. Science 315(5814):972\u2013976","journal-title":"Science"},{"issue":"5","key":"836_CR6","doi-asserted-by":"publisher","first-page":"345","DOI":"10.1016\/S0306-4379(00)00022-3","volume":"25","author":"S Guha","year":"2000","unstructured":"Guha S, Rastogi R, Shim K (2000) Rock: a robust clustering algorithm for categorical attributes. Inf Syst 25(5):345\u2013366","journal-title":"Inf Syst"},{"issue":"1","key":"836_CR7","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 (2001) Cure: an efficient clustering algorithm for large databases. Inf Syst 26(1):35\u201358","journal-title":"Inf Syst"},{"issue":"2","key":"836_CR8","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1016\/j.knosys.2014.03.001","volume":"63","author":"J Ha","year":"2014","unstructured":"Ha J, Seok S, Lee JS (2014) Robust outlier detection using the instability factor. Knowl Based Syst 63(2):15\u201323","journal-title":"Knowl Based Syst"},{"issue":"3","key":"836_CR9","doi-asserted-by":"publisher","first-page":"337","DOI":"10.1007\/s10994-016-5608-2","volume":"106","author":"J Huang","year":"2017","unstructured":"Huang J, Zhu Q, Yang L, Cheng D, Wu Q (2017) Qcc: a novel clustering algorithm based on quasi-cluster centers. Mach Learn 106(3):337\u2013357","journal-title":"Mach Learn"},{"issue":"3","key":"836_CR10","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 (1999) Data clustering: a review. Acm Comput Surv 31(3):264\u2013323","journal-title":"Acm Comput Surv"},{"issue":"1","key":"836_CR11","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1109\/92.748202","volume":"7","author":"G Karypis","year":"2002","unstructured":"Karypis G, Aggarwal R, Kumar V, Shekhar S (2002) Multilevel hypergraph partitioning: applications in vlsi domain. IEEE Trans Very Large Scale Integr Syst 7(1):69\u201379","journal-title":"IEEE Trans Very Large Scale Integr Syst"},{"key":"836_CR12","doi-asserted-by":"crossref","unstructured":"Karypis G, Han EH, Kumar V (1999) Chameleon: hierarchical clustering using dynamic modeling. IEEE Computer Society Press","DOI":"10.1109\/2.781637"},{"key":"836_CR13","volume-title":"Finding groups in data: an introduction to cluster analysis","author":"L Kaufman","year":"2009","unstructured":"Kaufman L, Rousseeuw PJ (2009) Finding groups in data: an introduction to cluster analysis. John Wiley, Hoboken"},{"issue":"317","key":"836_CR14","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1080\/01621459.1967.10482890","volume":"62","author":"B King","year":"1967","unstructured":"King B (1967) Step-wise clustering procedures. J Am Stat Assoc 62(317):86\u2013101","journal-title":"J Am Stat Assoc"},{"key":"836_CR15","first-page":"9","volume":"171C","author":"Y Lv","year":"2015","unstructured":"Lv Y, Ma T, Tang M, Cao J, Tian Y, Al-Rodhaan M (2015) An efficient and scalable density-based clustering algorithm for datasets with complex structures. Neurocomputing 171C:9\u201322","journal-title":"Neurocomputing"},{"key":"836_CR16","unstructured":"Macqueen J (1967) Some methods for classification and analysis of multivariate observations. In: Proceedings of Berkeley Symposium on Mathematical Statistics and Probability, pp 281\u2013297"},{"key":"836_CR17","doi-asserted-by":"publisher","unstructured":"Newman ME, Girvan M (2004) Finding and evaluating community structure in networks. Phys Rev E Stat Nonlinear Soft Matter Phys . \n                    https:\/\/doi.org\/10.1103\/PhysRevE.69.026113","DOI":"10.1103\/PhysRevE.69.026113"},{"issue":"5","key":"836_CR18","first-page":"1","volume":"70","author":"MEJ Newman","year":"2004","unstructured":"Newman MEJ (2004) Analysis of weighted networks. Phys Rev E Stat Nonlinear Soft Matter Phys 70(5):1\u20139","journal-title":"Phys Rev E Stat Nonlinear Soft Matter Phys"},{"issue":"23","key":"836_CR19","doi-asserted-by":"publisher","first-page":"8577","DOI":"10.1073\/pnas.0601602103","volume":"103","author":"MEJ Newman","year":"2006","unstructured":"Newman MEJ (2006) Modularity and community structure in networks. Proc Natl Acad Sci USA 103(23):8577\u20138582","journal-title":"Proc Natl Acad Sci USA"},{"issue":"6191","key":"836_CR20","doi-asserted-by":"publisher","first-page":"1492","DOI":"10.1126\/science.1242072","volume":"344","author":"A Rodriguez","year":"2014","unstructured":"Rodriguez A, Laio A (2014) Clustering by fast search and find of density peaks. Science 344(6191):1492","journal-title":"Science"},{"key":"836_CR21","doi-asserted-by":"publisher","first-page":"855","DOI":"10.1038\/193855a0","volume":"193","author":"PH Sneath","year":"1962","unstructured":"Sneath PH, Sokal RR (1962) Numerical taxonomy. Nature 193:855\u2013860","journal-title":"Nature"},{"issue":"9","key":"836_CR22","doi-asserted-by":"publisher","first-page":"1273","DOI":"10.1109\/TPAMI.2002.1033218","volume":"24","author":"CJ Veenman","year":"2002","unstructured":"Veenman CJ, Reinders MJT, Backer E (2002) A maximum variance cluster algorithm. IEEE Trans Pattern Anal Mach Intell 24(9):1273\u20131280","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"8","key":"836_CR23","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"},{"issue":"5","key":"836_CR24","doi-asserted-by":"publisher","first-page":"1523","DOI":"10.1016\/j.dsp.2013.03.009","volume":"23","author":"X Wang","year":"2013","unstructured":"Wang X, Wang XL, Chen C, Wilkes DM (2013) Enhancing minimum spanning tree-based clustering by removing density-based outliers. Digital Signal Process 23(5):1523\u20131538","journal-title":"Digital Signal Process"},{"key":"836_CR25","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1016\/j.ins.2016.03.011","volume":"354","author":"JY Xie","year":"2016","unstructured":"Xie JY, Gao HC, Xie WX, Liu XH, Grant PW (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"},{"issue":"3","key":"836_CR26","doi-asserted-by":"publisher","first-page":"304","DOI":"10.1109\/TKDE.2006.46","volume":"18","author":"H Xiong","year":"2006","unstructured":"Xiong H, Pandey G, Steinbach M, Kumar V (2006) Enhancing data analysis with noise removal. IEEE Trans Knowl Data Eng 18(3):304\u2013319","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"836_CR27","doi-asserted-by":"crossref","unstructured":"Zhang T, Ramakrishnan R, Livny M (1996) Birch: an efficient data clustering method for very large databases. In: ACM SIGMOD International Conference on Management of Data, pp 103\u2013114","DOI":"10.1145\/235968.233324"},{"key":"836_CR28","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1016\/j.patrec.2016.05.007","volume":"80","author":"Q Zhu","year":"2016","unstructured":"Zhu Q, Feng J, Huang J (2016) Natural neighbor: a self-adaptive neighborhood method without parameter k. Pattern Recognit Lett 80:30\u201336","journal-title":"Pattern Recognit Lett"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-018-0836-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s13042-018-0836-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-018-0836-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,6,28]],"date-time":"2019-06-28T19:35:50Z","timestamp":1561750550000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s13042-018-0836-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,6,29]]},"references-count":28,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2019,7]]}},"alternative-id":["836"],"URL":"https:\/\/doi.org\/10.1007\/s13042-018-0836-3","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,6,29]]},"assertion":[{"value":"20 September 2017","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 May 2018","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 June 2018","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}