{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T04:35:25Z","timestamp":1779338125250,"version":"3.51.4"},"reference-count":50,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2019,4,5]],"date-time":"2019-04-05T00:00:00Z","timestamp":1554422400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100004488","name":"Hrvatska Zaklada za Znanost","doi-asserted-by":"publisher","award":["IP-2016-06-6545"],"award-info":[{"award-number":["IP-2016-06-6545"]}],"id":[{"id":"10.13039\/501100004488","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004488","name":"Hrvatska Zaklada za Znanost","doi-asserted-by":"crossref","award":["IP-2016-06-8350"],"award-info":[{"award-number":["IP-2016-06-8350"]}],"id":[{"id":"10.13039\/501100004488","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Pattern Anal Applic"],"published-print":{"date-parts":[[2020,5]]},"DOI":"10.1007\/s10044-019-00809-z","type":"journal-article","created":{"date-parts":[[2019,4,5]],"date-time":"2019-04-05T16:14:00Z","timestamp":1554480840000},"page":"541-554","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":47,"title":["DBSCAN-like clustering method for various data densities"],"prefix":"10.1007","volume":"23","author":[{"given":"Rudolf","family":"Scitovski","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kristian","family":"Sabo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,4,5]]},"reference":[{"key":"809_CR1","series-title":"CRC data mining and knowledge discovery series","doi-asserted-by":"crossref","DOI":"10.1201\/b15410","volume-title":"Data clustering: algorithms and applications","author":"CC Aggarwall","year":"2013","unstructured":"Aggarwall CC, Reddy CK (2013) Data clustering: algorithms and applications. CRC data mining and knowledge discovery series. Chapman & Hall, London"},{"key":"809_CR2","doi-asserted-by":"crossref","first-page":"725","DOI":"10.1016\/j.patcog.2012.09.020","volume":"46","author":"C Akinlar","year":"2013","unstructured":"Akinlar C, Topal C (2013) Edcircles: a real-time circle detector with a false detection control. Pattern Recognit 46:725\u2013740","journal-title":"Pattern Recognit"},{"key":"809_CR3","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1016\/j.compeleceng.2016.08.021","volume":"57","author":"R Amami","year":"2017","unstructured":"Amami R, Smiti A (2017) An incremental method combining density clustering and support vector machines for voice pathology detection. Comput Electr Eng 57:257\u2013265","journal-title":"Comput Electr Eng"},{"key":"809_CR4","doi-asserted-by":"crossref","first-page":"369","DOI":"10.1016\/j.procs.2013.05.200","volume":"18","author":"G Andrade","year":"2013","unstructured":"Andrade G, Ramos G, Madeira D, Sachetto R, Ferreira R, Rocha L (2013) G-DBSCAN: a GPU accelerated algorithm for density-based clustering. Procedia Comput Sci 18:369\u2013378","journal-title":"Procedia Comput Sci"},{"key":"809_CR5","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1145\/304181.304187","volume":"28","author":"M Ankerst","year":"1999","unstructured":"Ankerst M, Breunig MM, Kriegel HP, Sander J (1999) OPTICS: ordering points to identify the clustering structure. ACM Sigmod Rec 28:49\u201360","journal-title":"ACM Sigmod Rec"},{"key":"809_CR6","doi-asserted-by":"crossref","first-page":"866","DOI":"10.1016\/j.patcog.2010.10.018","volume":"44","author":"AM Bagirov","year":"2011","unstructured":"Bagirov AM, Ugon J, Webb D (2011) Fast modified global \n$$k$$\n\n\n\nk\n\n\n\n-means algorithm for incremental cluster construction. Pattern Recognit 44:866\u2013876","journal-title":"Pattern Recognit"},{"key":"809_CR7","doi-asserted-by":"crossref","first-page":"1147","DOI":"10.1016\/j.aej.2015.08.009","volume":"54","author":"AM Bakr","year":"2015","unstructured":"Bakr AM, Ghanem NM, Ismail MA (2015) Efficient incremental density-based algorithm for clustering large datasets. Alex Eng J 54:1147\u20131154","journal-title":"Alex Eng J"},{"key":"809_CR8","volume-title":"Fuzzy models and algorithms for pattern recognition and image processing","author":"JC Bezdek","year":"2005","unstructured":"Bezdek JC, Keller J, Krisnapuram R, Pal NR (2005) Fuzzy models and algorithms for pattern recognition and image processing. Springer, New York"},{"key":"809_CR9","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1016\/j.datak.2006.01.013","volume":"60","author":"D Birant","year":"2007","unstructured":"Birant D, Kut A (2007) ST-DBSCAN: an algorithm for clustering spatial\u2013temporal data. Data Knowl Eng 60:208\u2013221","journal-title":"Data Knowl Eng"},{"issue":"2","key":"809_CR10","doi-asserted-by":"crossref","first-page":"553","DOI":"10.1214\/aos\/1031833664","volume":"25","author":"JA Cuesta-Albertos","year":"1997","unstructured":"Cuesta-Albertos JA, Gordaliza A, Matr\u00e1n C (1997) Trimmed \n$$k$$\n\n\n\nk\n\n\n\n-means: an attempt to robustify quantizers. Ann Stat 25(2):553\u2013576","journal-title":"Ann Stat"},{"key":"809_CR11","doi-asserted-by":"crossref","first-page":"1166","DOI":"10.1016\/j.phpro.2012.02.174","volume":"24","author":"H Darong","year":"2012","unstructured":"Darong H, Peng W (2012) Grid-based DBSCAN algorithm with referential parameters. Phys Procedia 24:1166\u20131170","journal-title":"Phys Procedia"},{"key":"809_CR12","doi-asserted-by":"crossref","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 second SIAM international conference on data mining, San Francisco","DOI":"10.1137\/1.9781611972733.5"},{"key":"809_CR13","unstructured":"Ester M, Krieogel H, Sander J (1996) A density-based algorithm for discovering clusters in large spatial databases with noise. In: 2nd International conference on knowledge discovery and data mining (KDD-96), Portland, pp 226\u2013231"},{"key":"809_CR14","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1007\/s10044-005-0240-y","volume":"8","author":"H Frigui","year":"2005","unstructured":"Frigui H (2005) Unsupervised learning of arbitrarily shaped clusters using ensembles of Gaussian models. Pattern Anal Appl 8:32\u201349","journal-title":"Pattern Anal Appl"},{"key":"809_CR15","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1016\/j.csda.2012.11.018","volume":"61","author":"H Fritz","year":"2013","unstructured":"Fritz H, Garc\u00eda-Escudero LA, Mayo-Iscar A (2013) A fast algorithm for robust constrained clustering. Comput Stat Data Anal 61:124\u2013136","journal-title":"Comput Stat Data Anal"},{"key":"809_CR16","doi-asserted-by":"crossref","first-page":"824","DOI":"10.1016\/j.patcog.2016.06.031","volume":"60","author":"R Grbi\u0107","year":"2016","unstructured":"Grbi\u0107 R, Grahovac D, Scitovski R (2016) A method for solving the multiple ellipses detection problem. Pattern Recognit 60:824\u2013834","journal-title":"Pattern Recognit"},{"key":"809_CR17","doi-asserted-by":"crossref","first-page":"1193","DOI":"10.1007\/s10898-012-0020-3","volume":"57","author":"R Grbi\u0107","year":"2013","unstructured":"Grbi\u0107 R, Nyarko EK, Scitovski R (2013) A modification of the DIRECT method for Lipschitz global optimization for a symmetric function. J Glob Optim 57:1193\u20131212","journal-title":"J Glob Optim"},{"key":"809_CR18","unstructured":"Gunawan A (2013). A Faster Algorithm for DBSCAN. Ph.D. thesis, Technische Universiteit Eindhoven"},{"key":"809_CR19","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1007\/BF01908075","volume":"2","author":"L Hubert","year":"1985","unstructured":"Hubert L, Arabie P (1985) Comparing partitions. J Classif 2:193\u2013218","journal-title":"J Classif"},{"key":"809_CR20","doi-asserted-by":"crossref","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, Wang X, Hu X (2011) A new hybrid method based on partitioning-based DBSCAN and ant clustering. Expert Syst Appl 38:9373\u20139381","journal-title":"Expert Syst Appl"},{"key":"809_CR21","doi-asserted-by":"crossref","first-page":"431","DOI":"10.1007\/0-306-48332-7_93","volume-title":"The encyclopedia of optimization","author":"DR Jones","year":"2001","unstructured":"Jones DR (2001) The direct global optimization algorithm. In: Floudas CA, Pardalos PM (eds) The encyclopedia of optimization. Kluwer Academic Publishers, Dordrect, pp 431\u2013440"},{"key":"809_CR22","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1007\/BF00941892","volume":"79","author":"DR Jones","year":"1993","unstructured":"Jones DR, Perttunen CD, Stuckman BE (1993) Lipschitzian optimization without the Lipschitz constant. J Optim Theory Appl 79:157\u2013181","journal-title":"J Optim Theory Appl"},{"key":"809_CR23","first-page":"1","volume":"91","author":"A Karami","year":"2014","unstructured":"Karami A, Johansson R (2014) Choosing DBSCAN parameters automatically using differential evolution. Int J Comput Appl 91:1\u201311","journal-title":"Int J Comput Appl"},{"key":"809_CR24","volume-title":"Introduction to clustering large and high-dimensional data","author":"J Kogan","year":"2007","unstructured":"Kogan J (2007) Introduction to clustering large and high-dimensional data. Cambridge University Press, New York"},{"key":"809_CR25","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.patcog.2016.03.008","volume":"58","author":"KM Kumar","year":"2016","unstructured":"Kumar KM, Reddy ARM (2016) A fast DBSCAN clustering algorithm by accelerating neighbor searching using groups method. Pattern Recognit 58:39\u201348","journal-title":"Pattern Recognit"},{"key":"809_CR26","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1007\/s10044-011-0261-7","volume":"15","author":"HP Lai","year":"2012","unstructured":"Lai HP, Visani M, Boucher A, Ogier JM (2012) An experimental comparison of clustering methods for content-based indexing of large image databases. Pattern Anal Appl 15:345\u2013366","journal-title":"Pattern Anal Appl"},{"key":"809_CR27","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1016\/j.bspc.2017.04.002","volume":"36","author":"Z Li","year":"2017","unstructured":"Li Z, Zhang Y, Gong H, Liu G, Li W, Tang X (2017) An automatic and efficient coronary arteries extraction method in CT angiographies. Biomed Signal Process Control 36:221\u2013233","journal-title":"Biomed Signal Process Control"},{"key":"809_CR28","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.patrec.2016.10.014","volume":"93","author":"S Louhichi","year":"2017","unstructured":"Louhichi S, Gzara M, Ben-Abdallah H (2017) Unsupervised varied density based clustering algorithm using spline. Pattern Recognit Lett 93:48\u201357","journal-title":"Pattern Recognit Lett"},{"key":"809_CR29","unstructured":"MacQueen JB (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"},{"key":"809_CR30","first-page":"53","volume":"4","author":"T Maro\u0161evi\u0107","year":"2013","unstructured":"Maro\u0161evi\u0107 T, Sabo K, Taler P (2013) A mathematical model for uniform distribution voters per constituencies. Croat Oper Res Rev 4:53\u201364","journal-title":"Croat Oper Res Rev"},{"key":"809_CR31","doi-asserted-by":"crossref","unstructured":"McCallum A, Nigam K, Ungar LH (2000) Efficient clustering of high-dimensional data sets with application to reference matching. In: International conference on knowledge discovery and data mining. DBLP","DOI":"10.1145\/347090.347123"},{"key":"809_CR32","doi-asserted-by":"crossref","first-page":"1572","DOI":"10.1016\/j.patrec.2011.06.003","volume":"32","author":"S Mimaroglu","year":"2011","unstructured":"Mimaroglu S, Aksehirli E (2011) Improving DBSCAN\u2019s execution time by using a pruning technique on bit vectors. Pattern Recognit Lett 32:1572\u20131580","journal-title":"Pattern Recognit Lett"},{"key":"809_CR33","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1016\/j.cageo.2014.09.003","volume":"73","author":"A Morales-Esteban","year":"2014","unstructured":"Morales-Esteban A, Mart\u00ednez-\u00c1lvarez F, Scitovski S, Scitovski R (2014) A fast partitioning algorithm using adaptive Mahalanobis clustering with application to seismic zoning. Comput Geosci 73:132\u2013141","journal-title":"Comput Geosci"},{"key":"809_CR34","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1016\/j.ins.2015.02.011","volume":"305","author":"K Sabo","year":"2015","unstructured":"Sabo K, Scitovski R (2015) An approach to cluster separability in a partition. Inf Sci 305:208\u2013218","journal-title":"Inf Sci"},{"key":"809_CR35","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1007\/s11590-011-0389-9","volume":"7","author":"K Sabo","year":"2013","unstructured":"Sabo K, Scitovski R, Vazler I (2013) One-dimensional center-based \n$$l_1$$\n\n\n\n\nl\n1\n\n\n\n\n-clustering method. Optim Lett 7:5\u201322","journal-title":"Optim Lett"},{"key":"809_CR36","doi-asserted-by":"crossref","first-page":"713","DOI":"10.1007\/s10898-017-0510-4","volume":"68","author":"R Scitovski","year":"2017","unstructured":"Scitovski R (2017) A new global optimization method for a symmetric Lipschitz continuous function and application to searching for a globally optimal partition of a one-dimensional set. J Glob Optim 68:713\u2013727","journal-title":"J Glob Optim"},{"key":"809_CR37","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.patrec.2014.09.010","volume":"52","author":"R Scitovski","year":"2014","unstructured":"Scitovski R, Maro\u0161evi\u0107 T (2014) Multiple circle detection based on center-based clustering. Pattern Recognit Lett 52:9\u201316","journal-title":"Pattern Recognit Lett"},{"key":"809_CR38","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.knosys.2013.11.010","volume":"57","author":"R Scitovski","year":"2014","unstructured":"Scitovski R, Sabo K (2014) Analysis of the \n$$k$$\n\n\n\nk\n\n\n\n-means algorithm in the case of data points occurring on the border of two or more clusters. Knowl Based Syst 57:1\u20137","journal-title":"Knowl Based Syst"},{"key":"809_CR39","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1016\/j.cageo.2013.06.010","volume":"59","author":"R Scitovski","year":"2013","unstructured":"Scitovski R, Scitovski S (2013) A fast partitioning algorithm and its application to earthquake investigation. Comput Geosci 59:124\u2013131","journal-title":"Comput Geosci"},{"key":"809_CR40","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1016\/j.eswa.2015.12.034","volume":"51","author":"R Scitovski","year":"2016","unstructured":"Scitovski R, Vidovi\u0107 I, Bajer D (2016) A new fast fuzzy partitioning algorithm. Expert Syst Appl 51:143\u2013150","journal-title":"Expert Syst Appl"},{"key":"809_CR41","volume-title":"Cluster-formation und analyse","author":"H Sp\u00e4th","year":"1983","unstructured":"Sp\u00e4th H (1983) Cluster-formation und analyse. R. Oldenburg Verlag, M\u00fcnchen"},{"key":"809_CR42","doi-asserted-by":"crossref","unstructured":"Steinbach M, Tan PN, Potter VKC, Klooster S (2002) Data mining for the discovery of ocean climate indices, In: Mining scientific datasets workshop, 2nd Annual SIAM international conference on data mining","DOI":"10.1145\/956750.956801"},{"key":"809_CR43","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1007\/3-540-28349-8_5","volume-title":"Grouping multidimensional data","author":"M Teboulle","year":"2006","unstructured":"Teboulle M, Berkhin P, Dhilon I, Guan Y, Kogan J (2006) Clustering with entropy-like \n$$k$$\n\n\n\nk\n\n\n\n-means algorithms. In: Kogan J, Nicholas C, Teboulle M (eds) Grouping multidimensional data. Springer, Berlin, pp 127\u2013160"},{"key":"809_CR44","volume-title":"Pattern recognition","author":"S Theodoridis","year":"2009","unstructured":"Theodoridis S, Koutroumbas K (2009) Pattern recognition, 4th edn. Academic Press, Burlington","edition":"4"},{"key":"809_CR45","unstructured":"Vendramin L, Campello RJGB, Hruschka ER (2009) On the comparison of relative clustering validity criteria, In: Proceedings of the SIAM international conference on data mining, SDM 2009, April 30\u2013May 2, 2009. SIAM, Sparks, pp 733\u2013744"},{"key":"809_CR46","doi-asserted-by":"crossref","first-page":"1477","DOI":"10.1016\/j.patrec.2009.08.008","volume":"30","author":"P Viswanath","year":"2009","unstructured":"Viswanath P, Babu VS (2009) Rough-DBSCAN: a fast hybrid density based clustering method for large data sets. Pattern Recognit Lett 30:1477\u20131488","journal-title":"Pattern Recognit Lett"},{"key":"809_CR47","unstructured":"Wolfram\u00a0Research I (2016) Mathematica, version 11.0 edition. Wolfram Research, Inc., Champaign"},{"key":"809_CR48","doi-asserted-by":"crossref","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 PW (2016) Robust clustering by detecting density peaks and assigning points based on fuzzy weighted \n$$K$$\n\n\n\nK\n\n\n\n-nearest neighbors. Inf Sci 354:19\u201340","journal-title":"Inf Sci"},{"key":"809_CR49","doi-asserted-by":"crossref","DOI":"10.1017\/CBO9780511810114","volume-title":"Data mining and analysis: fundamental concepts and algorithms","author":"MJ Zaki","year":"2014","unstructured":"Zaki MJ, Meira W Jr (2014) Data mining and analysis: fundamental concepts and algorithms. Cambridge University Press, New York"},{"key":"809_CR50","doi-asserted-by":"crossref","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 Recognit 60:983\u2013997","journal-title":"Pattern Recognit"}],"container-title":["Pattern Analysis and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10044-019-00809-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10044-019-00809-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10044-019-00809-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,4,22]],"date-time":"2020-04-22T10:05:46Z","timestamp":1587549946000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10044-019-00809-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,4,5]]},"references-count":50,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2020,5]]}},"alternative-id":["809"],"URL":"https:\/\/doi.org\/10.1007\/s10044-019-00809-z","relation":{},"ISSN":["1433-7541","1433-755X"],"issn-type":[{"value":"1433-7541","type":"print"},{"value":"1433-755X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,4,5]]},"assertion":[{"value":"9 December 2017","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 March 2019","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 April 2019","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}