{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T16:40:17Z","timestamp":1743093617148,"version":"3.40.3"},"publisher-location":"New York, NY","reference-count":28,"publisher":"Springer New York","isbn-type":[{"type":"print","value":"9781493971305"},{"type":"electronic","value":"9781493971312"}],"license":[{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018]]},"DOI":"10.1007\/978-1-4939-7131-2_138","type":"book-chapter","created":{"date-parts":[[2018,6,11]],"date-time":"2018-06-11T19:23:22Z","timestamp":1528745002000},"page":"206-219","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Clustering Algorithms"],"prefix":"10.1007","author":[{"given":"Davide","family":"Eynard","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marco Alberto","family":"Javarone","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Matteo","family":"Matteucci","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2018,6,12]]},"reference":[{"key":"138_CR375","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1186\/2194-3206-1-5","volume":"1","author":"G Armano","year":"2013","unstructured":"Armano G, Javarone MA (2013) Clustering datasets by complex networks analysis. Complex Adapt Syst Model 1:5. https:\/\/doi.org\/10.1186\/2194-3206-1-5","journal-title":"Complex Adapt Syst Model"},{"key":"138_CR376","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4757-0450-1","volume-title":"Pattern recognition with fuzzy objective function algorithms","author":"JC Bezdek","year":"1981","unstructured":"Bezdek JC (1981) Pattern recognition with fuzzy objective function algorithms. Kluwer, Norwell"},{"key":"138_CR377","doi-asserted-by":"publisher","DOI":"10.1088\/1742-5468\/2008\/10\/P10008","volume":"2008","author":"V Blondel","year":"2008","unstructured":"Blondel V, Guillaume J, Lambiotte R, Lefebvre E (2008) Fast unfolding of communities in large network. J Stat Mech Theory Exp 2008:P10008","journal-title":"J Stat Mech Theory Exp"},{"key":"138_CR378","unstructured":"Boyd D, Crawford K (2011) Six provocations for big data. Social Science Research Network working paper series. http:\/\/ssrn.com\/abstract=1926431"},{"key":"138_CR379","first-page":"58","volume":"4","author":"R D\u2019Andrade","year":"1978","unstructured":"D\u2019Andrade R (1978) U-statistic hierarchical clustering. Psychometrika 4:58\u201367","journal-title":"Psychometrika"},{"issue":"1","key":"138_CR380","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 39(1):1\u201338","journal-title":"J R Stat Soc Ser B"},{"issue":"3","key":"138_CR381","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1080\/01969727308-546046","volume":"3","author":"JC Dunn","year":"1973","unstructured":"Dunn JC (1973) A fuzzy relative of the isodata process and its use in detecting compact well-separated clusters. J Cybern 3(3):32\u201357. https:\/\/doi.org\/10.1080\/01969727308-546046","journal-title":"J Cybern"},{"key":"138_CR382","unstructured":"Ester M, Kriegel HP, Sander J, Xu X (1996) A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of the 2nd international conference on knowledge discovery and data mining. AAAI Press, Portland, pp 226\u2013231"},{"issue":"3-5","key":"138_CR383","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1016\/j.physrep.2009.11.002","volume":"486","author":"Santo Fortunato","year":"2010","unstructured":"Fortunato S (2010) Community detection in graphs. Phys Rep 486(3\u20135):75\u2013174. https:\/\/doi.org\/10.1016\/j.physrep.2009. 11.002. http:\/\/www.sciencedirect.com\/science\/article\/B6TVP-4XPYXF1-1\/2\/99061fac6435db4343b2374d2%206e64ac1","journal-title":"Physics Reports"},{"issue":"12","key":"138_CR384","doi-asserted-by":"publisher","first-page":"7821","DOI":"10.1073\/pnas.122653799","volume":"99","author":"M Girvan","year":"2002","unstructured":"Girvan M, Newman MEJ (2002) Community structure in social and biological networks. Proc Natl Acad Sci 99(12):7821\u20137826","journal-title":"Proc Natl Acad Sci"},{"key":"138_CR385","volume-title":"Data mining: concepts and techniques","author":"J Han","year":"2000","unstructured":"Han J, Kamber M (2000) Data mining: concepts and techniques. Morgan Kaufmann, San Francisco\/London"},{"key":"138_CR386","volume-title":"Clustering algorithms. Probability & mathematical statistics","author":"JA Hartigan","year":"1975","unstructured":"Hartigan JA (1975) Clustering algorithms. Probability & mathematical statistics. Wiley, New York"},{"key":"138_CR387","series-title":"Springer series in statistics","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-84858-7","volume-title":"The elements of statistical learning: data mining, inference, and prediction","author":"TJ Hastie","year":"2009","unstructured":"Hastie TJ, Tibshirani RJ, Friedman JH (2009) The elements of statistical learning: data mining, inference, and prediction, Springer series in statistics. Springer, New York"},{"issue":"8","key":"138_CR388","doi-asserted-by":"publisher","first-page":"651","DOI":"10.1016\/j.patrec.2009.09.011","volume":"31","author":"AK Jain","year":"2010","unstructured":"Jain AK (2010) Data clustering: 50 years beyond k-means. Pattern Recogn Lett 31(8):651\u2013666","journal-title":"Pattern Recogn Lett"},{"issue":"3","key":"138_CR389","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. https:\/\/doi.org\/10.1145\/331499.331504","journal-title":"ACM Comput Surv"},{"issue":"11","key":"138_CR390","doi-asserted-by":"publisher","first-page":"1025","DOI":"10.1109\/T-C.1973.223640","volume":"22","author":"RA Jarvis","year":"1973","unstructured":"Jarvis RA, Patrick EA (1973) Clustering using a similarity measure based on shared near neighbors. IEEE Trans Comput 22(11):1025\u20131034. https:\/\/doi.org\/10.1109\/T-C.1973.223640","journal-title":"IEEE Trans Comput"},{"key":"138_CR391","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1007\/BF02289588","volume":"2","author":"SC Johnson","year":"1967","unstructured":"Johnson SC (1967) Hierarchical clustering schemes. Psychometrika 2:241\u2013254","journal-title":"Psychometrika"},{"key":"138_CR392","first-page":"405","volume":"1","author":"L Kaufman","year":"1987","unstructured":"Kaufman L, Rousseeuw P (1987) Clustering by means of medoids. Stat Data Anal Based L1 Norm Relat Methods 1:405\u2013416","journal-title":"Stat Data Anal Based L1 Norm Relat Methods"},{"issue":"2","key":"138_CR393","doi-asserted-by":"publisher","first-page":"291","DOI":"10.1002\/j.1538-7305.1970.tb01770.x","volume":"49","author":"BW Kernighan","year":"1970","unstructured":"Kernighan BW, Lin S (1970) An efficient heuristic procedure for partitioning graphs. Bell Syst Tech J 49(2):291\u2013308","journal-title":"Bell Syst Tech J"},{"issue":"4","key":"138_CR394","doi-asserted-by":"publisher","first-page":"373","DOI":"10.1093\/comjnl\/9.4.373","volume":"9","author":"GN Lance","year":"1967","unstructured":"Lance GN, Williams WT (1967) A general theory of classificatory sorting strategies 1. Hierarchical systems. Comput J 9(4):373\u2013380","journal-title":"Comput J"},{"issue":"2","key":"138_CR395","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1109\/TIT.1982.1056489","volume":"28","author":"S Lloyd","year":"1982","unstructured":"Lloyd S (1982) Least squares quantization in PCM. IEEE Trans Inf Theory 28(2):129\u2013137. https:\/\/doi.org\/10.1109\/TIT.1982.1056489","journal-title":"IEEE Trans Inf Theory"},{"issue":"4","key":"138_CR396","doi-asserted-by":"publisher","first-page":"395","DOI":"10.1007\/s11222-007-9033-z","volume":"17","author":"U Luxburg","year":"2007","unstructured":"Luxburg U (2007) A tutorial on spectral clustering. Stat Comput 17(4):395\u2013416. https:\/\/doi.org\/10.1007\/s11222-007-9033-z","journal-title":"Stat Comput"},{"key":"138_CR397","first-page":"849","volume-title":"Advances in neural information processing systems 14","author":"A Ng","year":"2002","unstructured":"Ng A, Jordan M, Weiss Y (2002) On spectral clustering: analysis and an algorithm. In: Dietterich T, Becker S, Ghahramani Z (eds) Advances in neural information processing systems 14. MIT Press, Cambridge, MA, pp 849\u2013856"},{"key":"138_CR398","doi-asserted-by":"crossref","unstructured":"Salvador S, Chan P (2004) Determining the number of clusters\/segments in hierarchical clustering\/segmentation algorithms. In: Proceedings of the 16th IEEE international conference on tools with artificial intelligence, ICTAI\u201904, Boca Raton. IEEE Computer Society, Washington, DC, pp 576\u2013584. http:\/\/dx.doi.org\/10.1109\/ICTAI.2004.50","DOI":"10.1109\/ICTAI.2004.50"},{"issue":"8","key":"138_CR399","doi-asserted-by":"publisher","first-page":"888","DOI":"10.1109\/34.868688","volume":"22","author":"J Shi","year":"2000","unstructured":"Shi J, Malik J (2000) Normalized cuts and image segmentation. IEEE Trans on Pattn Anal and Mach Intell 22(8):888\u2013905","journal-title":"IEEE Trans on Pattn Anal and Mach Intell"},{"key":"138_CR3100","volume-title":"Introduction to data mining","author":"PN Tan","year":"2005","unstructured":"Tan PN, Steinbach M, Kumar V (2005) Introduction to data mining, 1st edn. Addison-Wesley Longman Publishing Co., Inc., Boston","edition":"1"},{"key":"138_CR3101","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1007\/978-94-017-0115-0_5","volume-title":"Communities and technologies","author":"JR Tyler","year":"2003","unstructured":"Tyler JR, Wilkinson DM, Huberman BA (2003) Email as spectroscopy: automated discovery of community structure within organizations. In: Communities and technologies. Springer Netherlands, Deventer, pp 81\u201396"},{"issue":"1","key":"138_CR3102","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1109\/T-C.1971.223083","volume":"20","author":"CT Zahn","year":"1971","unstructured":"Zahn CT (1971) Graph-theoretical methods for detecting and describing gestalt clusters. IEEE Trans Comput 20(1):68\u201386. https:\/\/doi.org\/10.1109\/T-C.1971.223083","journal-title":"IEEE Trans Comput"}],"container-title":["Encyclopedia of Social Network Analysis and Mining"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-1-4939-7131-2_138","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,7]],"date-time":"2024-07-07T11:51:26Z","timestamp":1720353086000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-1-4939-7131-2_138"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018]]},"ISBN":["9781493971305","9781493971312"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-1-4939-7131-2_138","relation":{},"subject":[],"published":{"date-parts":[[2018]]},"assertion":[{"value":"12 June 2018","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}}]}}