{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,22]],"date-time":"2024-10-22T15:32:46Z","timestamp":1729611166044,"version":"3.28.0"},"reference-count":63,"publisher":"IEEE","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2015,10]]},"DOI":"10.1109\/bigdata.2015.7363964","type":"proceedings-article","created":{"date-parts":[[2015,12,28]],"date-time":"2015-12-28T16:36:21Z","timestamp":1451320581000},"page":"1891-1900","source":"Crossref","is-referenced-by-count":2,"title":["Multi-probe random projection clustering to secure very large distributed datasets"],"prefix":"10.1109","author":[{"given":"Lee A.","family":"Carraher","sequence":"first","affiliation":[]},{"given":"Philip A.","family":"Wilsey","sequence":"additional","affiliation":[]},{"given":"Anindya","family":"Moitra","sequence":"additional","affiliation":[]},{"given":"Sayantan","family":"Dey","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611973082.68"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-73435-2_9"},{"key":"ref33","first-page":"226","article-title":"A density-based algorithm for discovering clusters in large spatial databases with noise","volume":"96","author":"ester","year":"1996","journal-title":"KDD"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1145\/1542362.1542419"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2002.800499"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/18.556675"},{"key":"ref37","article-title":"Scaling and convergence of projection sampling","author":"florescu","year":"2009","journal-title":"Tech Rep"},{"key":"ref36","first-page":"143","article-title":"Experiments with random projection","author":"dasgupta","year":"2000","journal-title":"Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence ser UAI'00 San Francisco"},{"key":"ref35","article-title":"Random projections for k-means clustering","volume":"abs 1011 4632","author":"boutsidis","year":"2010","journal-title":"CoRR"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1145\/276304.276314"},{"key":"ref60","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1609\/aimag.v29i3.2157","article-title":"Collective classification in network data","volume":"29","author":"sen","year":"2008","journal-title":"AI Magazine"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1145\/301136.301186"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1109\/ISDA.2009.9"},{"key":"ref63","doi-asserted-by":"crossref","first-page":"952","DOI":"10.1097\/CCM.0b013e31820a92c6","article-title":"Multiparameter intelligent monitoring in intensive care ii (mimic-ii): A public-access intensive care unit database","volume":"39","author":"saeed","year":"2011","journal-title":"Critical Care Medicine"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.4153\/CJM-1967-017-0"},{"journal-title":"Sphere Packings Lattices and Groups Third Edition","year":"1998","author":"conway","key":"ref27"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/18.335970"},{"journal-title":"Foundations of Multidimensional and Metric Data Structures","year":"2006","author":"samet","key":"ref2"},{"journal-title":"Cluster Analysis for Applications","year":"1973","author":"anderberg","key":"ref1"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1145\/502512.502546"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1145\/10515.10549"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-50335-8_31"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511807077"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-44842-X_84"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1017\/S0305004100052075"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1145\/997817.997857"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/18.556675"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.4007\/annals.2009.170.1003"},{"key":"ref59","first-page":"10","article-title":"Streaming k-means approximation","author":"ailon","year":"2009","journal-title":"Advances in Neural Information Processing Systems 22"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/17.2.126"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.2307\/2346830"},{"key":"ref56","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1007\/3-540-44598-6_3","article-title":"Lecture notes in computer science","volume":"1880","author":"lindell","year":"2000","journal-title":"Advances in Cryptology ? CRYPTO 2000"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2008.33"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/CASoN.2010.139"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1126\/science.1229566"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1145\/1109557.1109688"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-46502-2_13"},{"key":"ref11","first-page":"341","article-title":"Distributed data mining: Algorithms, systems, and applications","author":"park","year":"2002","journal-title":"Data Mining Handbook"},{"key":"ref40","first-page":"186","article-title":"Random projection for high dimensional data clustering: A cluster ensemble approach","author":"fern","year":"2003","journal-title":"Machine Learning Proceedings of the Twentieth International Conference (ICML 2003)"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1145\/509593.509642"},{"key":"ref13","doi-asserted-by":"crossref","first-page":"274","DOI":"10.1007\/978-3-642-00202-1_24","article-title":"Lecture notes in computer science","volume":"5431","author":"mahajan","year":"2009","journal-title":"WALCOM Algorithms and Computation"},{"key":"ref14","first-page":"217","article-title":"When is&#x201D;nearest neighbor&#x201D; meaningful?","author":"beyer","year":"1999","journal-title":"Intl Conf on Database Theory"},{"key":"ref15","article-title":"The hardness of k-means clustering","author":"dasgupta","year":"2008","journal-title":"Univ of California San Diego Dep of Computer Sci and Engineering"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1007\/BF02776078"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1145\/375551.375608"},{"journal-title":"The Random Projection Method ser DIMACS Series","year":"2004","author":"vempala","key":"ref18"},{"key":"ref19","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1016\/0095-8956(88)90043-3","article-title":"The johnson-lindenstrauss lemma and the sphericity of some graphs","volume":"44","author":"frankl","year":"1987","journal-title":"J Comb Theory Ser A"},{"key":"ref4","first-page":"36","article-title":"Latent class models for clustering: A comparison with k-means","volume":"20","author":"magidson","year":"2002","journal-title":"Canadian Journal of Marketing Research"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2005.845141"},{"key":"ref6","article-title":"Towards a statistical theory of clustering","author":"luxburg","year":"0","journal-title":"In PASCAL workshop on Statistics and Optimization of Clustering 2005"},{"key":"ref5","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1023\/A:1007617005950","article-title":"Unsupervised learning by probabilistic latent semantic analysis","volume":"42","author":"hofmann","year":"2001","journal-title":"Mach Learn"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1145\/304182.304188"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2005.27"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/26.494294"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2002.1033770"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1145\/2483699.2483701"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1145\/1806689.1806737"},{"key":"ref48","doi-asserted-by":"crossref","first-page":"1097","DOI":"10.1109\/18.391252","article-title":"The leech lattice, the octacode, and decoding algorithms","volume":"41","author":"sun","year":"1995","journal-title":"Information Theory IEEE Transactions on"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/18.412695"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2008.07.014"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611972764.23"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1145\/1132516.1132597"},{"article-title":"Nearest neighbor search: the old, the new, and the impossible","year":"2009","author":"andoni","key":"ref43"}],"event":{"name":"2015 IEEE International Conference on Big Data (Big Data)","start":{"date-parts":[[2015,10,29]]},"location":"Santa Clara, CA, USA","end":{"date-parts":[[2015,11,1]]}},"container-title":["2015 IEEE International Conference on Big Data (Big Data)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/7347101\/7363706\/07363964.pdf?arnumber=7363964","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,9,2]],"date-time":"2019-09-02T20:36:44Z","timestamp":1567456604000},"score":1,"resource":{"primary":{"URL":"http:\/\/ieeexplore.ieee.org\/document\/7363964\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015,10]]},"references-count":63,"URL":"https:\/\/doi.org\/10.1109\/bigdata.2015.7363964","relation":{},"subject":[],"published":{"date-parts":[[2015,10]]}}}