{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T08:23:35Z","timestamp":1781252615805,"version":"3.54.1"},"reference-count":35,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2018,5,8]],"date-time":"2018-05-08T00:00:00Z","timestamp":1525737600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["NRF2017R1D1A3B03035729"],"award-info":[{"award-number":["NRF2017R1D1A3B03035729"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Supercomput"],"published-print":{"date-parts":[[2019,1]]},"DOI":"10.1007\/s11227-018-2380-z","type":"journal-article","created":{"date-parts":[[2018,5,8]],"date-time":"2018-05-08T12:10:30Z","timestamp":1525781430000},"page":"142-169","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":69,"title":["AA-DBSCAN: an approximate adaptive DBSCAN for finding clusters with varying densities"],"prefix":"10.1007","volume":"75","author":[{"given":"Jeong-Hun","family":"Kim","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jong-Hyeok","family":"Choi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kwan-Hee","family":"Yoo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aziz","family":"Nasridinov","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2018,5,8]]},"reference":[{"key":"2380_CR1","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1016\/j.neucom.2015.05.109","volume":"171","author":"Y Lv","year":"2016","unstructured":"Lv Y, Ma T, Tang M et al (2016) An efficient and scalable density-based clustering algorithm for datasets with complex structures. Neurocomputing 171:9\u201322. \n                    https:\/\/doi.org\/10.1016\/j.neucom.2015.05.109","journal-title":"Neurocomputing"},{"key":"2380_CR2","volume-title":"Data mining: concepts and techniques","author":"J Han","year":"2011","unstructured":"Han J, Pei J, Kamber M (2011) Data mining: concepts and techniques. Morgan Kaufmann, Waltham"},{"key":"2380_CR3","doi-asserted-by":"publisher","first-page":"983","DOI":"10.1016\/j.patcog.2016.07.007","volume":"60","author":"Y Zhu","year":"2016","unstructured":"Zhu Y, Ting KM, Carman MJ (2016) Density-ratio based clustering for discovering clusters with varying densities. Pattern Recogn 60:983\u2013997. \n                    https:\/\/doi.org\/10.1016\/j.patcog.2016.07.007","journal-title":"Pattern Recogn"},{"issue":"34","key":"2380_CR4","first-page":"226","volume":"96","author":"M Ester","year":"1996","unstructured":"Ester M, Kriegel H-P, Sander J, Xu X (1996) A density-based algorithm for discovering clusters in large spatial databases with noise. Kdd 96(34):226\u2013231","journal-title":"Kdd"},{"key":"2380_CR5","doi-asserted-by":"publisher","unstructured":"Wang X, Hamilton HJ (2003) DBRS: a density-based spatial clustering method with random sampling. In: Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp 563\u2013575. \n                    https:\/\/doi.org\/10.1007\/3-540-36175-8_56","DOI":"10.1007\/3-540-36175-8_56"},{"key":"2380_CR6","doi-asserted-by":"publisher","unstructured":"Roy S, Bhattacharyya DK (2005) An approach to find embedded clusters using density based techniques. In: International Conference on Distributed Computing and Internet Technology, pp 523\u2013535. \n                    https:\/\/doi.org\/10.1007\/11604655_59","DOI":"10.1007\/11604655_59"},{"issue":"6","key":"2380_CR7","doi-asserted-by":"publisher","first-page":"509","DOI":"10.1007\/BF02948834","volume":"15","author":"A Zhou","year":"2000","unstructured":"Zhou A, Zhou S, Cao J et al (2000) Approaches for scaling DBSCAN algorithm to large spatial databases. J Comput Sci Technol 15(6):509\u2013526. \n                    https:\/\/doi.org\/10.1007\/BF02948834","journal-title":"J Comput Sci Technol"},{"issue":"10","key":"2380_CR8","first-page":"2739","volume":"9","author":"Z Xiong","year":"2012","unstructured":"Xiong Z, Chen R, Zhang Y, Zhang X (2012) Multi-density DBSCAN algorithm based on density levels partitioning. J Inform Comput Sci 9(10):2739\u20132749","journal-title":"J Inform Comput Sci"},{"key":"2380_CR9","doi-asserted-by":"publisher","unstructured":"El-Sonbaty Y, Ismail MA, Farouk M (2004) An efficient density based clustering algorithm for large databases. In: 16th IEEE International Conference on Tools with Artificial Intelligence, pp 673\u2013677. \n                    https:\/\/doi.org\/10.1109\/ictai.2004.27","DOI":"10.1109\/ictai.2004.27"},{"key":"2380_CR10","doi-asserted-by":"publisher","unstructured":"Xiaoyun C, Yufang M, Yan Z, Ping W (2008) GMDBSCAN: multi-density DBSCAN cluster based on grid. In: IEEE International Conference on e-Business Engineering, pp 780\u2013783. \n                    https:\/\/doi.org\/10.1109\/ICEBE.2008.54","DOI":"10.1109\/ICEBE.2008.54"},{"issue":"8","key":"2380_CR11","doi-asserted-by":"publisher","first-page":"9373","DOI":"10.1016\/j.eswa.2011.01.135","volume":"38","author":"H Jiang","year":"2011","unstructured":"Jiang H, Li J, Yi S et al (2011) A new hybrid method based on partitioning-based DBSCAN and ant clustering. Expert Syst Appl 38(8):9373\u20139381. \n                    https:\/\/doi.org\/10.1016\/j.eswa.2011.01.135","journal-title":"Expert Syst Appl"},{"issue":"7","key":"2380_CR12","doi-asserted-by":"publisher","first-page":"973","DOI":"10.1016\/j.patrec.2011.02.001","volume":"32","author":"X Chen","year":"2011","unstructured":"Chen X, Liu W, Qiu H, Lai J (2011) APSCAN: a parameter free algorithm for clustering. Pattern Recogn Lett 32(7):973\u2013986. \n                    https:\/\/doi.org\/10.1016\/j.patrec.2011.02.001","journal-title":"Pattern Recogn Lett"},{"issue":"7","key":"2380_CR13","doi-asserted-by":"publisher","first-page":"3182","DOI":"10.1109\/TIP.2016.2559803","volume":"25","author":"J Hou","year":"2016","unstructured":"Hou J, Gao H, Li X (2016) DSets-DBSCAN: a parameter-free clustering algorithm. IEEE Trans Image Process 25(7):3182\u20133193. \n                    https:\/\/doi.org\/10.1109\/TIP.2016.2559803","journal-title":"IEEE Trans Image Process"},{"issue":"2","key":"2380_CR14","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1145\/304181.304187","volume":"28","author":"M Ankerst","year":"1999","unstructured":"Ankerst M, Breunig MM, Kriegel H-P, Sander J (1999) OPTICS: ordering points to identify the clustering structure. ACM Sigmod Rec 28(2):49\u201360. \n                    https:\/\/doi.org\/10.1145\/304182.304187","journal-title":"ACM Sigmod Rec"},{"key":"2380_CR15","doi-asserted-by":"publisher","unstructured":"Liu P, Zhou D, Wu N (2007) VDBSCAN: varied density based spatial clustering of applications with noise. In: International Conference on Service Systems and Service Management, pp 1\u20134. \n                    https:\/\/doi.org\/10.1109\/ICSSSM.2007.4280175","DOI":"10.1109\/ICSSSM.2007.4280175"},{"issue":"6","key":"2380_CR16","doi-asserted-by":"publisher","first-page":"2939","DOI":"10.1016\/j.eswa.2013.10.025","volume":"41","author":"S Jahirabadkar","year":"2014","unstructured":"Jahirabadkar S, Kulkarni P (2014) Algorithm to determine \u03b5-distance parameter in density based clustering. Expert Syst Appl 41(6):2939\u20132946. \n                    https:\/\/doi.org\/10.1016\/j.eswa.2013.10.025","journal-title":"Expert Syst Appl"},{"key":"2380_CR17","doi-asserted-by":"publisher","first-page":"375","DOI":"10.1109\/AICI.2009.393","volume":"4","author":"TQ Huang","year":"2009","unstructured":"Huang TQ, Yu YQ, Li K, Zeng WF (2009) Reckon the parameter of dbscan for multi-density data sets with constraints. Int Conf Artif Intell Comput Intell 4:375\u2013379. \n                    https:\/\/doi.org\/10.1109\/AICI.2009.393","journal-title":"Int Conf Artif Intell Comput Intell"},{"issue":"3","key":"2380_CR18","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1023\/A:1009884809343","volume":"3","author":"X Xu","year":"1999","unstructured":"Xu X, J\u00e4ger J, Kriegel H-P (1999) A fast parallel clustering algorithm for large spatial databases. Data Min Knowl Disccov 3(3):263\u2013290. \n                    https:\/\/doi.org\/10.1007\/0-306-47011-X_3","journal-title":"Data Min Knowl Disccov"},{"key":"2380_CR19","first-page":"501","volume":"3","author":"ED Lumer","year":"1994","unstructured":"Lumer ED, Faieta B (1994) Diversity and adaptation in populations of clustering ants. Proc Third Int Conf Simul Adapt Behav 3:501\u2013508","journal-title":"Proc Third Int Conf Simul Adapt Behav"},{"issue":"1","key":"2380_CR20","first-page":"100","volume":"28","author":"JA Hartigan","year":"1979","unstructured":"Hartigan JA, Wong MA (1979) Algorithm AS 136: a k-means clustering algorithm. J Roy Stat Soc Ser C (Appl Stat) 28(1):100\u2013108","journal-title":"J Roy Stat Soc Ser C (Appl Stat)"},{"key":"2380_CR21","doi-asserted-by":"publisher","DOI":"10.1111\/exsy.12204","author":"O Limwattanapibool","year":"2017","unstructured":"Limwattanapibool O, Arch-int S (2017) Determination of the appropriate parameters for K-means clustering using selection of region clusters based on density DBSCAN (SRCD-DBSCAN). Expert Syst. \n                    https:\/\/doi.org\/10.1111\/exsy.12204","journal-title":"Expert Syst"},{"key":"2380_CR22","doi-asserted-by":"publisher","unstructured":"Ert\u00f6z L, Steinbach M, Kumar V (2003) Finding clusters of different sizes, shapes, and densities in noisy, high dimensional data. In: Proceedings of the 2003 SIAM International Conference on Data Mining, pp 47\u201358. \n                    https:\/\/doi.org\/10.1137\/1.9781611972733.5","DOI":"10.1137\/1.9781611972733.5"},{"issue":"6191","key":"2380_CR23","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. \n                    https:\/\/doi.org\/10.1126\/science.1242072","journal-title":"Science"},{"issue":"5","key":"2380_CR24","doi-asserted-by":"publisher","first-page":"603","DOI":"10.1109\/34.1000236","volume":"24","author":"D Comaniciu","year":"2002","unstructured":"Comaniciu D, Meer P (2002) Mean shift: a robust approach toward feature space analysis. IEEE Trans Pattern Anal Mach Intell 24(5):603\u2013619. \n                    https:\/\/doi.org\/10.1109\/34.1000236","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"2","key":"2380_CR25","doi-asserted-by":"publisher","first-page":"1313","DOI":"10.1007\/s10586-017-0818-3","volume":"20","author":"X Liu","year":"2017","unstructured":"Liu X, Yang Q, He L (2017) A novel DBSCAN with entropy and probability for mixed data. Cluster Comput 20(2):1313\u20131323. \n                    https:\/\/doi.org\/10.1007\/s10586-017-0818-3","journal-title":"Cluster Comput"},{"issue":"2","key":"2380_CR26","doi-asserted-by":"publisher","first-page":"1135","DOI":"10.1007\/s10586-017-0763-1","volume":"20","author":"J Kim","year":"2017","unstructured":"Kim J, Lee W, Song JJ, Lee SB (2017) Optimized combinatorial clustering for stochastic processes. Cluster Comput 20(2):1135\u20131148. \n                    https:\/\/doi.org\/10.1007\/s10586-017-0763-1","journal-title":"Cluster Comput"},{"issue":"3","key":"2380_CR27","doi-asserted-by":"publisher","first-page":"157","DOI":"10.14778\/3021924.3021932","volume":"10","author":"A Lulli","year":"2016","unstructured":"Lulli A, Dell\u2019Amico M, Michiardi P, Ricci L (2016) NG-DBSCAN: scalable density-based clustering for arbitrary data. Proc VLDB Endow 10(3):157\u2013168. \n                    https:\/\/doi.org\/10.14778\/3021924.3021932","journal-title":"Proc VLDB Endow"},{"key":"2380_CR28","doi-asserted-by":"crossref","unstructured":"Dalli A (2003) Adaptation of the F-measure to cluster based lexicon quality evaluation. In: Proceedings of the EACL 2003 Workshop on Evaluation Initiatives in Natural Language Processing: Are Evaluation Methods, Metrics and Resources Reusable? pp 51\u201356","DOI":"10.3115\/1641396.1641404"},{"issue":"7","key":"2380_CR29","doi-asserted-by":"publisher","first-page":"978","DOI":"10.1016\/j.is.2006.10.006","volume":"32","author":"L Duan","year":"2007","unstructured":"Duan L, Xu L, Guo F et al (2007) A local-density based spatial clustering algorithm with noise. Inform Syst 32(7):978\u2013986. \n                    https:\/\/doi.org\/10.1016\/j.is.2006.10.006","journal-title":"Inform Syst"},{"key":"2380_CR30","unstructured":"Machine Learning. Clustering datasets (2016) \n                    http:\/\/cs.joensuu.fi\/sipu\/datasets"},{"key":"2380_CR31","unstructured":"Frank A, Asuncion A (2010) UCI machine learning repository. \n                    http:\/\/archive.ics.uci.edu\/ml"},{"key":"2380_CR32","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. \n                    https:\/\/doi.org\/10.1016\/j.knosys.2017.07.010","journal-title":"Knowl Based Syst"},{"issue":"2","key":"2380_CR33","doi-asserted-by":"publisher","first-page":"322","DOI":"10.1145\/93605.98741","volume":"19","author":"N Beckmann","year":"1990","unstructured":"Beckmann N, Kriegel H-P, Schneider R, Seeger B (1990) The R*-tree: an efficient and robust access method for points and rectangles. ACM Sigmod Rec 19(2):322\u2013331. \n                    https:\/\/doi.org\/10.1145\/93597.98741","journal-title":"ACM Sigmod Rec"},{"key":"2380_CR34","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1016\/j.ins.2014.10.023","volume":"308","author":"WK Loh","year":"2015","unstructured":"Loh WK, Yu H (2015) Fast density-based clustering through dataset partition using graphics processing units. Inf Sci 308:94\u2013112. \n                    https:\/\/doi.org\/10.1016\/j.ins.2014.10.023","journal-title":"Inf Sci"},{"key":"2380_CR35","doi-asserted-by":"publisher","first-page":"369","DOI":"10.1016\/j.procs.2013.05.200","volume":"18","author":"G Andrade","year":"2013","unstructured":"Andrade G, Ramos G et al (2013) G-dbscan: a gpu accelerated algorithm for density-based clustering. Proc Comput Sci 18:369\u2013378. \n                    https:\/\/doi.org\/10.1016\/j.procs.2013.05.200","journal-title":"Proc Comput Sci"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s11227-018-2380-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-018-2380-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-018-2380-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,5,9]],"date-time":"2019-05-09T16:29:38Z","timestamp":1557419378000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s11227-018-2380-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,5,8]]},"references-count":35,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2019,1]]}},"alternative-id":["2380"],"URL":"https:\/\/doi.org\/10.1007\/s11227-018-2380-z","relation":{},"ISSN":["0920-8542","1573-0484"],"issn-type":[{"value":"0920-8542","type":"print"},{"value":"1573-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,5,8]]},"assertion":[{"value":"8 May 2018","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}