{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T16:11:46Z","timestamp":1780675906222,"version":"3.54.1"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2018,4,16]],"date-time":"2018-04-16T00:00:00Z","timestamp":1523836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"name":"the Fundamental Research Funds for the Central Universities","award":["2017XKQY076"],"award-info":[{"award-number":["2017XKQY076"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Knowl Inf Syst"],"published-print":{"date-parts":[[2019,5]]},"DOI":"10.1007\/s10115-018-1189-7","type":"journal-article","created":{"date-parts":[[2018,4,16]],"date-time":"2018-04-16T04:55:58Z","timestamp":1523854558000},"page":"285-309","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":40,"title":["A novel density peaks clustering with sensitivity of local density and density-adaptive metric"],"prefix":"10.1007","volume":"59","author":[{"given":"Mingjing","family":"Du","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shifei","family":"Ding","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Xue","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhongzhi","family":"Shi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2018,4,16]]},"reference":[{"key":"1189_CR1","doi-asserted-by":"crossref","unstructured":"Ankerst M, Breunig MM, Kriegel HP et al (1999) OPTICS: ordering points to identify the clustering structure. In: Proceedings of the ACM international conference on management of data, pp 49\u201360","DOI":"10.1145\/304182.304187"},{"issue":"1","key":"1189_CR2","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1109\/TPAMI.1981.4767051","volume":"3","author":"E Backer","year":"1981","unstructured":"Backer E, Jain AK (1981) A clustering performance measure based on fuzzy set decomposition. IEEE Trans Pattern Anal Mach Intell 3(1):66\u201375","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"1189_CR3","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 ZJ et al (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":"3","key":"1189_CR4","doi-asserted-by":"publisher","first-page":"459","DOI":"10.1007\/s13042-013-0183-3","volume":"5","author":"WJ Chen","year":"2014","unstructured":"Chen WJ, Shao YH, Hong N (2014) Laplacian smooth twin support vector machine for semi-supervised classification. Int J Mach Learn Cybern 5(3):459\u2013468","journal-title":"Int J Mach Learn Cybern"},{"key":"1189_CR5","doi-asserted-by":"publisher","first-page":"1015","DOI":"10.1016\/j.procs.2015.07.096","volume":"55","author":"Z Chen","year":"2015","unstructured":"Chen Z, Qi Z, Meng F et al (2015) Image segmentation via improving clustering algorithms with density and distance. Proc Comput Sci 55:1015\u20131022","journal-title":"Proc Comput Sci"},{"issue":"1","key":"1189_CR6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1111\/j.2517-6161.1977.tb01600.x","volume":"39","author":"AP Dempster","year":"1977","unstructured":"Dempster AP, Laird NM, Rubin DB (1977) Maximum likelihood from incomplete data via the EM algorithm. J R Stat Soc Ser B (Methodological) 39(1):1\u201338","journal-title":"J R Stat Soc Ser B (Methodological)"},{"key":"1189_CR7","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":"1189_CR8","unstructured":"Ester M, Kriegel HP, Sander J et al (1996) A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of second international conference on knowledge discovery and data mining, pp 226\u2013231"},{"issue":"18","key":"1189_CR9","doi-asserted-by":"publisher","first-page":"2378","DOI":"10.1016\/j.fss.2007.12.023","volume":"159","author":"A Fern\u00e1ndez","year":"2008","unstructured":"Fern\u00e1ndez A, Garc\u00eda S, del Jesus MJ et al (2008) A study of the behaviour of linguistic fuzzy rule based classification systems in the framework of imbalanced data-sets. Fuzzy Sets Syst 159(18):2378\u20132398","journal-title":"Fuzzy Sets Syst"},{"issue":"3","key":"1189_CR10","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1016\/S0933-3657(98)00028-1","volume":"13","author":"HA G\u00fcvenir","year":"1998","unstructured":"G\u00fcvenir HA, Demir\u00f6z G, Ilter N (1998) Learning differential diagnosis of erythemato-squamous diseases using voting feature intervals. Artif Intell Med 13(3):147\u2013165","journal-title":"Artif Intell Med"},{"key":"1189_CR11","doi-asserted-by":"publisher","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 et al (2014) Clustering in extreme learning machine feature space. Neurocomputing 128:88\u201395","journal-title":"Neurocomputing"},{"issue":"1","key":"1189_CR12","first-page":"45","volume":"1","author":"N Iam-On","year":"2014","unstructured":"Iam-On N, Boongoen T, Kongkotchawan N (2014) A new link-based method to ensemble clustering and cancer microarray data analysis. Int J Collab Intell 1(1):45\u201367","journal-title":"Int J Collab Intell"},{"key":"1189_CR13","doi-asserted-by":"crossref","unstructured":"Jain AK, Law MC (2005) Data clustering: a user\u2019s Dilemma. In: Proceedings of first international conference of the pattern recognition and machine intelligence, pp 20\u201322","DOI":"10.1007\/11590316_1"},{"issue":"7\u20138","key":"1189_CR14","doi-asserted-by":"publisher","first-page":"1557","DOI":"10.1007\/s00521-014-1628-7","volume":"25","author":"H Jia","year":"2014","unstructured":"Jia H, Ding S, Meng L et al (2014) A density-adaptive affinity propagation clustering algorithm based on spectral dimension reduction. Neural Comput Appl 25(7\u20138):1557\u20131567","journal-title":"Neural Comput Appl"},{"issue":"3","key":"1189_CR15","doi-asserted-by":"publisher","first-page":"479","DOI":"10.1007\/s13042-015-0439-1","volume":"7","author":"X Jiang","year":"2016","unstructured":"Jiang X, Zhang W (2016) Structure learning for weighted networks based on Bayesian nonparametric models. Int J Mach Learn Cybern 7(3):479\u2013489","journal-title":"Int J Mach Learn Cybern"},{"issue":"1","key":"1189_CR16","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1007\/BF00337288","volume":"43","author":"T Kohonen","year":"1982","unstructured":"Kohonen T (1982) Self-organized formation of topologically correct feature maps. Biol Cybern 43(1):59\u201369","journal-title":"Biol Cybern"},{"key":"1189_CR17","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 Recogn Lett 73:52\u201359","journal-title":"Pattern Recogn Lett"},{"key":"1189_CR18","doi-asserted-by":"crossref","unstructured":"Lu K, Xia S, Xia C (2015) Clustering based road detection method. In: Proceedings of the 34th Chinese control conference, pp 3874\u20133879","DOI":"10.1109\/ChiCC.2015.7260237"},{"key":"1189_CR19","doi-asserted-by":"publisher","first-page":"488","DOI":"10.1016\/j.neucom.2016.05.020","volume":"207","author":"T Ma","year":"2016","unstructured":"Ma T, Wang Y, Tang M et al (2016) LED: a fast overlapping communities detection algorithm based on structural clustering. Neurocomputing 207:488\u2013500","journal-title":"Neurocomputing"},{"key":"1189_CR20","unstructured":"MacQueen J (1967) Some methods for classification and analysis of multivariate observations. In: Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, pp 281\u2013297"},{"issue":"4","key":"1189_CR21","doi-asserted-by":"publisher","first-page":"570","DOI":"10.1287\/opre.43.4.570","volume":"43","author":"OL Mangasarian","year":"1995","unstructured":"Mangasarian OL, Street WN, Wolberg WH (1995) Breast cancer diagnosis and prognosis via linear programming. Oper Res 43(4):570\u2013577","journal-title":"Oper Res"},{"issue":"17","key":"1189_CR22","doi-asserted-by":"publisher","first-page":"3299","DOI":"10.19026\/rjaset.6.3638","volume":"6","author":"IB Mohamad","year":"2013","unstructured":"Mohamad IB, Usman D (2013) Standardization and its effects on k-means clustering algorithm. Res J Appl Sci Eng Technol 6(17):3299\u20133303","journal-title":"Res J Appl Sci Eng Technol"},{"key":"1189_CR23","unstructured":"Ng AY, Jordan MI, Weiss Y (2002) On spectral clustering: analysis and an algorithm. In: Proceedings of advances in neural information processing systems, pp 849\u2013856"},{"issue":"3","key":"1189_CR24","doi-asserted-by":"publisher","first-page":"675","DOI":"10.1109\/TBC.2016.2580920","volume":"62","author":"Z Pan","year":"2016","unstructured":"Pan Z, Lei J, Zhang Y et al (2016) Fast motion estimation based on content property for low-complexity H.265\/HEVC encoder. IEEE Trans Broadcast 62(3):675\u2013684","journal-title":"IEEE Trans Broadcast"},{"issue":"6191","key":"1189_CR25","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\u20131496","journal-title":"Science"},{"issue":"3","key":"1189_CR26","first-page":"262","volume":"10","author":"VG Sigillito","year":"1989","unstructured":"Sigillito VG, Wing SP, Hutton LV et al (1989) Classification of radar returns from the ionosphere using neural networks. Johns Hopkins APL Tech Dig 10(3):262\u2013266","journal-title":"Johns Hopkins APL Tech Dig"},{"issue":"1","key":"1189_CR27","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1016\/j.trit.2016.12.005","volume":"2","author":"B Wang","year":"2017","unstructured":"Wang B, Zhang J, Liu Y et al (2017) Density peaks clustering based integrate framework for multi-document summarization. CAAI Trans Intell Technol 2(1):26\u201330","journal-title":"CAAI Trans Intell Technol"},{"issue":"8","key":"1189_CR28","first-page":"1577","volume":"35","author":"L Wang","year":"2007","unstructured":"Wang L, Bo LF, Jiao LC (2007) Density-sensitive spectral clustering. Acta Electron Sin 35(8):1577\u20131581","journal-title":"Acta Electron Sin"},{"issue":"5","key":"1189_CR29","doi-asserted-by":"publisher","first-page":"511","DOI":"10.1001\/archsurg.1995.01430050061010","volume":"130","author":"WH Wolberg","year":"1995","unstructured":"Wolberg WH, Street WN, Heisey DM et al (1995) Computerized breast cancer diagnosis and prognosis from fine-needle aspirates. Arch Surg 130(5):511\u2013516","journal-title":"Arch Surg"},{"key":"1189_CR30","doi-asserted-by":"publisher","DOI":"10.1007\/s13042-016-0603-2","author":"X Xu","year":"2016","unstructured":"Xu X, Ding S, Du M et al (2016) DPCG: an efficient density peaks clustering algorithm based on grid. Int J Mach Learn Cybern. https:\/\/doi.org\/10.1007\/s13042-016-0603-2","journal-title":"Int J Mach Learn Cybern"},{"issue":"1","key":"1189_CR31","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1016\/j.trit.2016.03.004","volume":"1","author":"X Xu","year":"2016","unstructured":"Xu X, Law R, Chen W et al (2016) Forecasting tourism demand by extracting fuzzy Takagi\u2013Sugeno rules from trained SVMs. CAAI Trans Intell Technol 1(1):30\u201342","journal-title":"CAAI Trans Intell Technol"},{"issue":"5","key":"1189_CR32","doi-asserted-by":"publisher","first-page":"621","DOI":"10.1016\/j.knosys.2011.01.009","volume":"24","author":"P Yang","year":"2011","unstructured":"Yang P, Zhu Q, Huang B (2011) Spectral clustering with density sensitive similarity function. Knowl-Based Syst 24(5):621\u2013628","journal-title":"Knowl-Based Syst"},{"key":"1189_CR33","unstructured":"Zelnik-Manor L, Perona P (2004) Self-tuning spectral clustering. In: Proceedings of advances in neural information processing systems, pp 1601\u20131608"},{"key":"1189_CR34","doi-asserted-by":"publisher","unstructured":"Zhang W, Li J (2015) Extended fast search clustering algorithm: widely density clusters, no density peaks. https:\/\/doi.org\/10.5121\/csit.2015.50701 . arXiv preprint arXiv:1505.05610","DOI":"10.5121\/csit.2015.50701"},{"key":"1189_CR35","doi-asserted-by":"crossref","unstructured":"Zhang Y, Xia Y, Liu Y et al (2015) Clustering sentences with density peaks for multi-document summarization. In: Proceedings of human language technologies: the 2015 annual conference of the North American Chapter of the ACL, pp 1262\u20131267","DOI":"10.3115\/v1\/N15-1136"},{"issue":"3","key":"1189_CR36","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1016\/j.trit.2016.10.009","volume":"1","author":"Q Zhong","year":"2016","unstructured":"Zhong Q, Chen F (2016) Trajectory planning for biped robot walking on uneven terrain\u2013Taking stepping as an example. CAAI Trans Intell Technol 1(3):197\u2013209","journal-title":"CAAI Trans Intell Technol"},{"key":"1189_CR37","unstructured":"Zhou D, Bousquet O, Lal TN et al (2004) Learning with local and global consistency. In: Proceedings of advances in neural information processing systems, pp 321\u2013328"}],"container-title":["Knowledge and Information Systems"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10115-018-1189-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10115-018-1189-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10115-018-1189-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,6]],"date-time":"2024-07-06T01:16:45Z","timestamp":1720228605000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10115-018-1189-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,4,16]]},"references-count":37,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2019,5]]}},"alternative-id":["1189"],"URL":"https:\/\/doi.org\/10.1007\/s10115-018-1189-7","relation":{},"ISSN":["0219-1377","0219-3116"],"issn-type":[{"value":"0219-1377","type":"print"},{"value":"0219-3116","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,4,16]]},"assertion":[{"value":"11 July 2016","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 April 2018","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 April 2018","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}