{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T05:13:58Z","timestamp":1776402838994,"version":"3.51.2"},"reference-count":32,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2017,12,1]],"date-time":"2017-12-01T00:00:00Z","timestamp":1512086400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2017,12]]},"abstract":"<jats:p>Stream clustering is a fundamental problem in many streaming data analysis applications. Comparing to classical batch-mode clustering, there are two key challenges in stream clustering: (i) Given that input data are changing continuously, how to incrementally update their clustering results efficiently? (ii) Given that clusters continuously evolve with the evolution of data, how to capture the cluster evolution activities? Unfortunately, most of existing stream clustering algorithms can neither update the cluster result in real-time nor track the evolution of clusters.<\/jats:p>\n          <jats:p>\n            In this paper, we propose a stream clustering algorithm\n            <jats:italic>EDMStream<\/jats:italic>\n            by exploring the Evolution of Density Mountain. The\n            <jats:italic>density mountain<\/jats:italic>\n            is used to abstract the data distribution, the changes of which indicate data distribution evolution. We track the evolution of clusters by monitoring the changes of density mountains. We further provide efficient data structures and filtering schemes to ensure that the update of density mountains is in real-time, which makes online clustering possible. The experimental results on synthetic and real datasets show that, comparing to the state-of-the-art stream clustering algorithms, e.g., D-Stream, DenStream, DBSTREAM and MR-Stream, our algorithm is able to response to a cluster update much faster (say 7-15x faster than the best of the competitors) and at the same time achieve comparable cluster quality. Furthermore,\n            <jats:italic>EDMStream<\/jats:italic>\n            successfully captures the cluster evolution activities.\n          <\/jats:p>","DOI":"10.1145\/3186728.3164136","type":"journal-article","created":{"date-parts":[[2019,11,20]],"date-time":"2019-11-20T10:54:54Z","timestamp":1574247294000},"page":"393-405","source":"Crossref","is-referenced-by-count":35,"title":["Clustering stream data by exploring the evolution of density mountain"],"prefix":"10.14778","volume":"11","author":[{"given":"Shufeng","family":"Gong","sequence":"first","affiliation":[{"name":"Northeastern University, Shenyang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanfeng","family":"Zhang","sequence":"additional","affiliation":[{"name":"Northeastern University, Shenyang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ge","family":"Yu","sequence":"additional","affiliation":[{"name":"Northeastern University and Liaoning University, Shenyang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2017,12]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"https:\/\/arxiv.org\/pdf\/1710.00867.pdf.  https:\/\/arxiv.org\/pdf\/1710.00867.pdf."},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.5555\/1196418"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.5555\/1315451.1315460"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.14778\/2735471.2735476"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611972764.29"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.14778\/2733004.2733045"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/1281192.1281210"},{"key":"e_1_2_1_8_1","volume-title":"Introduction to Information Retrieval","author":"Manning H. S.","year":"1993","unstructured":"H. S. Christopher D. Manning , Prabhakar Raghavan. Introduction to Information Retrieval . Cambridge University Press , 1993 . H. S. Christopher D. Manning, Prabhakar Raghavan. Introduction to Information Retrieval. Cambridge University Press, 1993."},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.5555\/3001460.3001507"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/2723372.2737792"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/3035918.3064050"},{"issue":"6","key":"e_1_2_1_12_1","first-page":"1400","article-title":"Eddpc:an efficient distributed density peaks clustering algorithm","volume":"53","author":"Gong S.","year":"2016","unstructured":"S. Gong and Y. Zhang . Eddpc:an efficient distributed density peaks clustering algorithm . Computer Research and Development , 53 ( 6 ): 1400 -- 1409 , 2016 . S. Gong and Y. Zhang. Eddpc:an efficient distributed density peaks clustering algorithm. Computer Research and Development, 53(6):1400--1409, 2016.","journal-title":"Computer Research and Development"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2016.2522412"},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.14778\/2850583.2850593"},{"key":"e_1_2_1_15_1","volume-title":"SOStream: Self Organizing Density-Based Clustering Over Data Stream","author":"Isaksson C.","year":"2012","unstructured":"C. Isaksson , M. H. Dunham , and M. Hahsler . SOStream: Self Organizing Density-Based Clustering Over Data Stream . Springer Berlin Heidelberg , 2012 . C. Isaksson, M. H. Dunham, and M. Hahsler. SOStream: Self Organizing Density-Based Clustering Over Data Stream. Springer Berlin Heidelberg, 2012."},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10115-010-0342-8"},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/2020408.2020555"},{"key":"e_1_2_1_18_1","unstructured":"M. Lichman. UCI machine learning repository http:\/\/archive.ics.uci.edu\/ml 2013.  M. Lichman. UCI machine learning repository http:\/\/archive.ics.uci.edu\/ml 2013."},{"key":"e_1_2_1_19_1","first-page":"281","volume-title":"Proceedings of the Berkeley symposium on mathematical statistics and probability","author":"MacQueen J.","year":"1967","unstructured":"J. MacQueen Some methods for classification and analysis of multivariate observations . In Proceedings of the Berkeley symposium on mathematical statistics and probability , pages 281 -- 297 , 1967 . J. MacQueen et al. Some methods for classification and analysis of multivariate observations. In Proceedings of the Berkeley symposium on mathematical statistics and probability, pages 281--297, 1967."},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.3233\/IDA-2011-0512"},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.3152\/147154699781767710"},{"key":"e_1_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/2413097.2413148"},{"key":"e_1_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/ISWC.2012.13"},{"key":"e_1_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1126\/science.1242072"},{"key":"e_1_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/2522968.2522981"},{"key":"e_1_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/1150402.1150491"},{"key":"e_1_2_1_28_1","first-page":"130","volume-title":"Proceedings of the KDD","author":"Stolfo S. J.","year":"1999","unstructured":"S. J. Stolfo , W. Fan , W. Lee , A. Prodromidis , and P. K. Chan . Cost-based modeling and evaluation for data mining with application to fraud and intrusion detection: Results from the jam project . In Proceedings of the KDD , pages 130 -- 144 , 1999 . S. J. Stolfo, W. Fan, W. Lee, A. Prodromidis, and P. K. Chan. Cost-based modeling and evaluation for data mining with application to fraud and intrusion detection: Results from the jam project. In Proceedings of the KDD, pages 130--144, 1999."},{"key":"e_1_2_1_29_1","first-page":"27","volume-title":"Proceedings of the COMAD","author":"Vennam J. R.","year":"2005","unstructured":"J. R. Vennam and S. Vadapalli . Syndeca: A tool to generate synthetic datasets for evaluation of clustering algorithms . In Proceedings of the COMAD , pages 27 -- 36 , 2005 . J. R. Vennam and S. Vadapalli. Syndeca: A tool to generate synthetic datasets for evaluation of clustering algorithms. In Proceedings of the COMAD, pages 27--36, 2005."},{"key":"e_1_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/1552303.1552307"},{"key":"e_1_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/233269.233324"},{"key":"e_1_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2013.146"},{"key":"e_1_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2016.2609423"}],"container-title":["Proceedings of the VLDB Endowment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3186728.3164136","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3186728.3164136","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T19:07:31Z","timestamp":1750273651000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3186728.3164136"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,12]]},"references-count":32,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2017,12]]}},"alternative-id":["10.1145\/3186728.3164136"],"URL":"https:\/\/doi.org\/10.1145\/3186728.3164136","relation":{},"ISSN":["2150-8097"],"issn-type":[{"value":"2150-8097","type":"print"}],"subject":[],"published":{"date-parts":[[2017,12]]}}}