{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T02:18:39Z","timestamp":1730254719906,"version":"3.28.0"},"reference-count":30,"publisher":"IEEE","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,8]]},"DOI":"10.1109\/icpr.2018.8546208","type":"proceedings-article","created":{"date-parts":[[2018,11,30]],"date-time":"2018-11-30T00:17:38Z","timestamp":1543537058000},"page":"2100-2105","source":"Crossref","is-referenced-by-count":1,"title":["Stream Clustering with Dynamic Estimation of Emerging Local Densities"],"prefix":"10.1109","author":[{"given":"Ziyin","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gavriil","family":"Tsechpenakis","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref30","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1145\/235968.233324","article-title":"BIRCH: an efficient data clustering method for very large databases","author":"zhang","year":"1996","journal-title":"Proc ACM SIGMOD"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1007\/s11227-014-1225-7"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1145\/2020408.2020515"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-40450-4_41"},{"key":"ref13","doi-asserted-by":"crossref","first-page":"972","DOI":"10.1126\/science.1136800","article-title":"Clustering by passing messages between data points","volume":"315","author":"frey","year":"2007","journal-title":"Science"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1023\/B:VISI.0000042993.50813.60"},{"key":"ref15","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1007\/978-3-540-28608-0_8","article-title":"Clustering data streams","author":"guha","year":"2016","journal-title":"Data Stream Management"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098079"},{"key":"ref17","article-title":"Revisiting k-means: New algorithms via Bayesian nonparametrics","author":"kulis","year":"2011","journal-title":"Proc Int'l Conf on Machine Learning"},{"journal-title":"The MNIST Database of Handwritten Digits","year":"0","author":"lecun","key":"ref18"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611974317.7"},{"key":"ref28","first-page":"1385","article-title":"Sharing clusters among related groups: Hierarchical Dirichlet processes","author":"teh","year":"2005","journal-title":"Proc NIPS"},{"key":"ref4","first-page":"209","article-title":"Coresets for nonparametric estimation-the case of DP-means","author":"bachem","year":"2015","journal-title":"Proc Int'l Conf on Machine Learning"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref3","first-page":"1027","article-title":"k-means++: the advantages of careful seeding","author":"arthur","year":"2007","journal-title":"Proc ACM-SIAM Symp on Discrete Algorithms"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.14778\/2180912.2180915"},{"journal-title":"UCI Machine Learning Repository","year":"0","key":"ref29"},{"key":"ref5","first-page":"1459","article-title":"Approximate K-Mcans++ in Sublinear Time","author":"bachem","year":"2016","journal-title":"Proc AAAI Conf on Artificial Intelligence"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611972764.29"},{"key":"ref7","first-page":"1995","article-title":"Distributed k-means and k-median Clustering on General Topologies","author":"balcan","year":"2013","journal-title":"Proc NIPS"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/B978-012722442-8\/50016-1"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/34.1000236"},{"key":"ref1","first-page":"2","article-title":"StreamKM++: A clustering algorithm for data streams","volume":"17","author":"ackermann","year":"2012","journal-title":"J of Exnerimental Algoriihmics"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/79.543975"},{"key":"ref22","doi-asserted-by":"crossref","first-page":"1492","DOI":"10.1126\/science.1242072","article-title":"Clustering by fast search and find of density peaks","volume":"344","author":"rodriguez","year":"2014","journal-title":"Science"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2002.994785"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/ACV.1994.341300"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-015-0816-y"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1145\/2522968.2522981"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1145\/1772690.1772862"}],"event":{"name":"2018 24th International Conference on Pattern Recognition (ICPR)","start":{"date-parts":[[2018,8,20]]},"location":"Beijing","end":{"date-parts":[[2018,8,24]]}},"container-title":["2018 24th International Conference on Pattern Recognition (ICPR)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/8527858\/8545020\/08546208.pdf?arnumber=8546208","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,27]],"date-time":"2022-01-27T13:57:52Z","timestamp":1643291872000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8546208\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,8]]},"references-count":30,"URL":"https:\/\/doi.org\/10.1109\/icpr.2018.8546208","relation":{},"subject":[],"published":{"date-parts":[[2018,8]]}}}