{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T07:18:58Z","timestamp":1740122338747,"version":"3.37.3"},"reference-count":54,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2018,4,10]],"date-time":"2018-04-10T00:00:00Z","timestamp":1523318400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/100008398","name":"Villum Fonden","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100008398","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Data Min Knowl Disc"],"published-print":{"date-parts":[[2018,7]]},"DOI":"10.1007\/s10618-018-0562-1","type":"journal-article","created":{"date-parts":[[2018,4,9]],"date-time":"2018-04-09T22:39:27Z","timestamp":1523313567000},"page":"1121-1176","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Anytime parallel density-based clustering"],"prefix":"10.1007","volume":"32","author":[{"given":"Son T.","family":"Mai","sequence":"first","affiliation":[]},{"given":"Ira","family":"Assent","sequence":"additional","affiliation":[]},{"given":"Jon","family":"Jacobsen","sequence":"additional","affiliation":[]},{"given":"Martin Storgaard","family":"Dieu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2018,4,10]]},"reference":[{"volume-title":"Data clustering: algorithms and applications","year":"2014","key":"562_CR1","unstructured":"Aggarwal CC, Reddy CK (eds) (2014) Data clustering: algorithms and applications. CRC Press, Boca Raton"},{"key":"562_CR2","doi-asserted-by":"crossref","unstructured":"Ankerst M, Breunig MM, Kriegel HP, Sander J (1999) OPTICS: ordering points to identify the clustering structure. In: International conference on management of data (SIGMOD), pp 49\u201360","DOI":"10.1145\/304182.304187"},{"key":"562_CR3","doi-asserted-by":"crossref","unstructured":"Arlia D, Coppola M (2001) Experiments in parallel clustering with DBSCAN. In: International Euro-par conference, pp 326\u2013331","DOI":"10.1007\/3-540-44681-8_46"},{"key":"562_CR4","doi-asserted-by":"crossref","unstructured":"Assent I, Kranen P, Baldauf C, Seidl T (2012) AnyOut: anytime outlier detection on streaming data. In: International conference on database systems for advanced applications (DASFAA) (1), pp 228\u2013242","DOI":"10.1007\/978-3-642-29038-1_18"},{"issue":"9","key":"562_CR5","doi-asserted-by":"publisher","first-page":"509","DOI":"10.1145\/361002.361007","volume":"18","author":"JL Bentley","year":"1975","unstructured":"Bentley JL (1975) Multidimensional binary search trees used for associative searching. Commun ACM 18(9):509\u2013517","journal-title":"Commun ACM"},{"key":"562_CR6","doi-asserted-by":"crossref","unstructured":"B\u00f6hm C, Noll R, Plant C, Wackersreuther B (2009) Density-based clustering using graphics processors. In: International conference on information and knowledge management (CIKM), pp 661\u2013670","DOI":"10.1145\/1645953.1646038"},{"key":"562_CR7","doi-asserted-by":"crossref","unstructured":"B\u00f6hm C, Feng J, He X, Mai ST, Plant C, Shao J (2011) A novel similarity measure for fiber clustering using longest common subsequence. In: Proceedings of the 2011 workshop on data mining for medicine and healthcare, pp 1\u20139","DOI":"10.1145\/2023582.2023584"},{"key":"562_CR8","doi-asserted-by":"crossref","unstructured":"Borah B, Bhattacharyya DK (2004) An improved sampling-based DBSCAN for large spatial databases. In: International conference on intelligent sensing and information processing (ICISIP), pp 92\u201396","DOI":"10.1109\/ICISIP.2004.1287631"},{"key":"562_CR9","doi-asserted-by":"crossref","unstructured":"Brecheisen S, Kriegel H, Pfeifle M (2004) Efficient density-based clustering of complex objects. In: IEEE international conference on data mining (ICDM), pp 43\u201350","DOI":"10.1109\/ICDM.2004.10082"},{"key":"562_CR10","doi-asserted-by":"crossref","unstructured":"Brecheisen S, Kriegel H, Pfeifle M (2006a) Parallel density-based clustering of complex objects. In: Pacific-Asia conference on knowledge discovery and data mining (PAKDD), pp 179\u2013188","DOI":"10.1007\/11731139_22"},{"issue":"3","key":"562_CR11","first-page":"284","volume":"9","author":"S Brecheisen","year":"2006","unstructured":"Brecheisen S, Kriegel HP, Pfeifle M (2006b) Multi-step density-based clustering. Knowl. Inf Syst 9(3):284\u2013308","journal-title":"Inf Syst"},{"key":"562_CR12","doi-asserted-by":"crossref","unstructured":"Chen L, Ng RT (2004) On the marriage of Lp-norms and edit distance. In: Very large data bases (VLDB), pp 792\u2013803","DOI":"10.1016\/B978-012088469-8.50070-X"},{"key":"562_CR13","volume-title":"Introduction to algorithms","author":"TH Cormen","year":"2009","unstructured":"Cormen TH, Leiserson CE, Rivest RL, Stein C (2009) Introduction to algorithms, 3rd edn. MIT Press, Cambridge","edition":"3"},{"key":"562_CR14","doi-asserted-by":"crossref","unstructured":"Dai BR, Lin IC (2012) Efficient map\/reduce-based DBSCAN algorithm with optimized data partition. In: IEEE international conference on cloud computing (CLOUD), pp 59\u201366","DOI":"10.1109\/CLOUD.2012.42"},{"key":"562_CR15","doi-asserted-by":"crossref","unstructured":"Dash M, Liu H, Xu X (2001) \u2018\n \n \n \n $$1 + 1 > 2$$\n \n \n \n 1\n +\n 1\n >\n 2\n \n \n \n \u2019: Merging distance and density based clustering. In: International conference on database systems for advanced applications (DASFAA), pp 32\u201339","DOI":"10.1109\/DASFAA.2001.916361"},{"key":"562_CR16","unstructured":"Ester M, Kriegel HP, Sander J, Xu X (1996) A density-based algorithm for discovering clusters in large spatial databases with noise. In: ACM SIGKDD international conference on knowledge discovery and data mining (KDD), pp 226\u2013231"},{"key":"562_CR17","doi-asserted-by":"publisher","first-page":"618","DOI":"10.1016\/j.snb.2015.03.028","volume":"215","author":"J Fonollosa","year":"2015","unstructured":"Fonollosa J, Sheik S, Huerta R, Marco S (2015) Reservoir computing compensates slow response of chemosensor arrays exposed to fast varying gas concentrations in continuous monitoring. Sensors Actuators B: Chem 215:618\u2013629","journal-title":"Sensors Actuators B: Chem"},{"issue":"3","key":"562_CR18","doi-asserted-by":"publisher","first-page":"265","DOI":"10.1016\/j.cmpb.2010.12.012","volume":"101","author":"Z Francis","year":"2011","unstructured":"Francis Z, Villagrasa C, Clairand I (2011) Simulation of DNA damage clustering after proton irradiation using an adapted DBSCAN algorithm. Comput Methods Programs Biomed 101(3):265\u2013270","journal-title":"Comput Methods Programs Biomed"},{"key":"562_CR19","doi-asserted-by":"crossref","unstructured":"Gan J, Tao Y (2015) DBSCAN revisited: mis-claim, un-fixability, and approximation. In: International conference on management of data (SIGMOD), pp 519\u2013530","DOI":"10.1145\/2723372.2737792"},{"issue":"3","key":"562_CR20","doi-asserted-by":"publisher","first-page":"14:1","DOI":"10.1145\/3083897","volume":"42","author":"J Gan","year":"2017","unstructured":"Gan J, Tao Y (2017) On the hardness and approximation of Euclidean DBSCAN. ACM Trans Database Syst 42(3):14:1\u201314:45","journal-title":"ACM Trans Database Syst"},{"key":"562_CR21","doi-asserted-by":"crossref","unstructured":"G\u00f6tz M, Bodenstein C, Riedel M (2015) HPDBSCAN: highly parallel DBSCAN. In: Proceedings of the workshop on machine learning in high-performance computing environments, pp 2:1\u20132:10","DOI":"10.1145\/2834892.2834894"},{"key":"562_CR22","doi-asserted-by":"crossref","unstructured":"Greiner J (1994) A comparison of parallel algorithms for connected components. In: Proceedings of the 6th annual ACM symposium on parallel algorithms and architectures (SSPA), pp 16\u201325","DOI":"10.1145\/181014.181021"},{"key":"562_CR23","unstructured":"Gunawan A (2013) A faster algorithm for DBSCAN. Msc thesis, TU Eindhoven"},{"key":"562_CR24","doi-asserted-by":"crossref","unstructured":"He Y, Tan H, Luo W, Mao H, Ma D, Feng S, Fan J (2011) MR-DBSCAN: an efficient parallel density-based clustering algorithm using MapReduce. In: International conference on parallel and distributed systems (ICPADS), pp 473\u2013480","DOI":"10.1109\/ICPADS.2011.83"},{"key":"562_CR25","doi-asserted-by":"crossref","unstructured":"Januzaj E, Kriegel HP, Pfeifle M (2004) Scalable density-based distributed clustering. In: European conference on principles of data mining and knowledge discovery (PKDD), pp 231\u2013244","DOI":"10.1007\/978-3-540-30116-5_23"},{"key":"562_CR26","doi-asserted-by":"crossref","unstructured":"Kobayashi T, Iwamura M, Matsuda T, Kise K (2013) An anytime algorithm for camera-based character recognition. In: International conference on document analysis and recognition (ICDAR), pp 1140\u20131144","DOI":"10.1109\/ICDAR.2013.231"},{"key":"562_CR27","doi-asserted-by":"crossref","unstructured":"Kriegel HP, Pfeifle M (2005) Density-based clustering of uncertain data. In: ACM SIGKDD international conference on knowledge discovery and data mining (KDD), pp 672\u2013677","DOI":"10.1145\/1081870.1081955"},{"issue":"2","key":"562_CR28","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1007\/s10115-016-1004-2","volume":"52","author":"H Kriegel","year":"2017","unstructured":"Kriegel H, Schubert E, Zimek A (2017) The (black) art of runtime evaluation: are we comparing algorithms or implementations? Knowl Inf Syst 52(2):341\u2013378","journal-title":"Knowl Inf Syst"},{"key":"562_CR29","volume-title":"Introduction to parallel computing","author":"V Kumar","year":"2002","unstructured":"Kumar V (2002) Introduction to parallel computing, 2nd edn. Addison-Wesley, Boston","edition":"2"},{"key":"562_CR30","doi-asserted-by":"crossref","unstructured":"Li T, Heinis T, Luk W (2016) Hashing-based approximate DBSCAN. In: Symposium on advances in databases and information systems (ADBIS), pp 31\u201345","DOI":"10.1007\/978-3-319-44039-2_3"},{"issue":"1","key":"562_CR31","doi-asserted-by":"publisher","first-page":"105","DOI":"10.15388\/Informatica.2017.122","volume":"28","author":"T Li","year":"2017","unstructured":"Li T, Heinis T, Luk W (2017) ADvaNCE\u2014efficient and scalable approximate density-based clustering based on hashing. Informatica 28(1):105\u2013130","journal-title":"Informatica"},{"issue":"3","key":"562_CR32","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 (PVLDB) 10(3):157\u2013168","journal-title":"Proc VLDB Endow (PVLDB)"},{"key":"562_CR33","doi-asserted-by":"crossref","unstructured":"Mahran S, Mahar K (2008) Using grid for accelerating density-based clustering. In: IEEE international conference on computer and information technology (CIT), pp 35\u201340","DOI":"10.1109\/CIT.2008.4594646"},{"key":"562_CR34","doi-asserted-by":"crossref","unstructured":"Mai ST, Goebl S, Plant C (2012) A similarity model and segmentation algorithm for white matter fiber tracts. In: IEEE international conference on data mining (ICDM), pp 1014\u20131019","DOI":"10.1109\/ICDM.2012.95"},{"key":"562_CR35","doi-asserted-by":"crossref","unstructured":"Mai ST, He X, Feng J, B\u00f6hm C (2013a) Efficient anytime density-based clustering. In: SIAM international conference on data mining (SDM), pp 112\u2013120","DOI":"10.1137\/1.9781611972832.13"},{"key":"562_CR36","doi-asserted-by":"crossref","unstructured":"Mai ST, He X, Hubig N, Plant C, B\u00f6hm C (2013b) Active density-based clustering. In: IEEE international conference on data mining (ICDM), pp 508\u2013517","DOI":"10.1109\/ICDM.2013.39"},{"issue":"2","key":"562_CR37","doi-asserted-by":"publisher","first-page":"319","DOI":"10.1007\/s10115-014-0797-0","volume":"45","author":"ST Mai","year":"2015","unstructured":"Mai ST, He X, Feng J, Plant C, B\u00f6hm C (2015) Anytime density-based clustering of complex data. Knowl Inf Syst 45(2):319\u2013355","journal-title":"Knowl Inf Syst"},{"key":"562_CR38","doi-asserted-by":"crossref","unstructured":"Mai ST, Assent I, Le A (2016a) Anytime OPTICS: an efficient approach for hierarchical density-based clustering. In: International conference on database systems for advanced applications (DASFAA), pp 164\u2013179","DOI":"10.1007\/978-3-319-32025-0_11"},{"key":"562_CR39","doi-asserted-by":"crossref","unstructured":"Mai ST, Assent I, Storgaard M (2016b) AnyDBC: an efficient anytime density-based clustering algorithm for very large complex datasets. In: ACM SIGKDD international conference on knowledge discovery and data mining (KDD), pp 1025\u20131034","DOI":"10.1145\/2939672.2939750"},{"key":"562_CR40","doi-asserted-by":"crossref","unstructured":"Mai ST, Dieu MS, Assent I, Jacobsen J, Kristensen J, Birk M (2017) Scalable and interactive graph clustering algorithm on multicore CPUs. In: IEEE international conference on data engineering (ICDE), pp 349\u2013360","DOI":"10.1109\/ICDE.2017.94"},{"key":"562_CR41","doi-asserted-by":"crossref","unstructured":"Patwary MMA, Palsetia D, Agrawal A, Liao W, Manne F, Choudhary AN (2012) A new scalable parallel DBSCAN algorithm using the disjoint-set data structure. In: Proceedings of the international conference on high performance computing, networking, storage and analysis (SC), p 62","DOI":"10.1109\/SC.2012.9"},{"key":"562_CR42","doi-asserted-by":"crossref","unstructured":"Patwary MMA, Satish N, Sundaram N, Manne F, Habib S, Dubey P (2014) Pardicle: parallel approximate density-based clustering. In: Proceedings of the international conference on high performance computing, networking, storage and analysis (SC), pp 560\u2013571","DOI":"10.1109\/SC.2014.51"},{"key":"562_CR43","doi-asserted-by":"crossref","unstructured":"Reiss A, Stricker D (2012) Introducing a new benchmarked dataset for activity monitoring. In: International symposium on wearable computers (ISWC), pp 108\u2013109","DOI":"10.1109\/ISWC.2012.13"},{"key":"562_CR44","doi-asserted-by":"crossref","unstructured":"Sakai T, Tamura K, Kitakami H (2017) Cell-based DBSCAN algorithm using minimum bounding rectangle criteria. In: International conference on database systems for advanced applications (DASFAA), pp 133\u2013144","DOI":"10.1007\/978-3-319-55705-2_10"},{"issue":"2","key":"562_CR45","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1023\/A:1009745219419","volume":"2","author":"J Sander","year":"1998","unstructured":"Sander J, Ester M, Kriegel HP, Xu X (1998) Density-based clustering in spatial databases: the algorithm GDBSCAN and its applications. Data Min Knowl Discov 2(2):169\u2013194","journal-title":"Data Min Knowl Discov"},{"issue":"3","key":"562_CR46","doi-asserted-by":"publisher","first-page":"19:1","DOI":"10.1145\/3068335","volume":"42","author":"E Schubert","year":"2017","unstructured":"Schubert E, Sander J, Ester M, Kriegel H, Xu X (2017) DBSCAN revisited, revisited: why and how you should (still) use DBSCAN. ACM Trans Database Syst 42(3):19:1\u201319:21","journal-title":"ACM Trans Database Syst"},{"key":"562_CR47","doi-asserted-by":"publisher","first-page":"A138","DOI":"10.1051\/0004-6361\/201220133","volume":"549","author":"A Tramacere","year":"2013","unstructured":"Tramacere A, Vecchio C (2013) \n \n \n \n $$\\gamma $$\n \n \n \u03b3\n \n \n -Ray DBSCAN: a clustering algorithm applied to fermi-LAT \n \n \n \n $$\\gamma $$\n \n \n \u03b3\n \n \n -ray data\u2014I. Detection performances with real and simulated data. Astron Astrophys 549:A138","journal-title":"Astron Astrophys"},{"key":"562_CR48","doi-asserted-by":"crossref","unstructured":"Vinh NX, Epps J, Bailey J (2009) Information theoretic measures for clusterings comparison: is a correction for chance necessary? In: International conference on machine learning (ICML), pp 1073\u20131080","DOI":"10.1145\/1553374.1553511"},{"key":"562_CR49","doi-asserted-by":"crossref","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 (PAKDD), pp 563\u2013575","DOI":"10.1007\/3-540-36175-8_56"},{"issue":"3","key":"562_CR50","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 HP (1999) A fast parallel clustering algorithm for large spatial databases. Data Min Knowl Discov 3(3):263\u2013290","journal-title":"Data Min Knowl Discov"},{"key":"562_CR51","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511810114","volume-title":"Data mining and analysis: fundamental concepts and algorithms","author":"MJ Zaki","year":"2014","unstructured":"Zaki MJ, M W Jr (2014) Data mining and analysis: fundamental concepts and algorithms. Cambridge University Press, New York"},{"issue":"5","key":"562_CR52","doi-asserted-by":"publisher","first-page":"881","DOI":"10.1016\/j.atmosenv.2007.10.024","volume":"42","author":"W Zhao","year":"2008","unstructured":"Zhao W, Hopke PK, Prather KA (2008) Comparison of two cluster analysis methods using single particle mass spectra. Atmos Environ 42(5):881\u2013892","journal-title":"Atmos Environ"},{"key":"562_CR53","doi-asserted-by":"crossref","unstructured":"Zhou S, Zhou A, Cao J, Jin W, Fan Y, Hu Y (2000) Combining sampling technique with DBSCAN algorithm for clustering large spatial databases. In: Pacific-Asia conference on knowledge discovery and data mining (PAKDD), pp 169\u2013172","DOI":"10.1007\/3-540-45571-X_20"},{"issue":"3","key":"562_CR54","first-page":"73","volume":"17","author":"S Zilberstein","year":"1996","unstructured":"Zilberstein S (1996) Using anytime algorithms in intelligent systems. AI Mag 17(3):73\u201383","journal-title":"AI Mag"}],"container-title":["Data Mining and Knowledge Discovery"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10618-018-0562-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10618-018-0562-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10618-018-0562-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,4,9]],"date-time":"2019-04-09T20:04:54Z","timestamp":1554840294000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10618-018-0562-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,4,10]]},"references-count":54,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2018,7]]}},"alternative-id":["562"],"URL":"https:\/\/doi.org\/10.1007\/s10618-018-0562-1","relation":{},"ISSN":["1384-5810","1573-756X"],"issn-type":[{"type":"print","value":"1384-5810"},{"type":"electronic","value":"1573-756X"}],"subject":[],"published":{"date-parts":[[2018,4,10]]},"assertion":[{"value":"30 November 2016","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 March 2018","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 April 2018","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}