{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,20]],"date-time":"2026-06-20T01:24:38Z","timestamp":1781918678711,"version":"3.54.5"},"reference-count":22,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2009,7,1]],"date-time":"2009-07-01T00:00:00Z","timestamp":1246406400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/100000015","name":"U.S. Department of Energy","doi-asserted-by":"publisher","award":["ER25737"],"award-info":[{"award-number":["ER25737"]}],"id":[{"id":"10.13039\/100000015","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000145","name":"Division of Information and Intelligent Systems","doi-asserted-by":"publisher","award":["IIS-0713109"],"award-info":[{"award-number":["IIS-0713109"]}],"id":[{"id":"10.13039\/100000145","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["60673060"],"award-info":[{"award-number":["60673060"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004608","name":"National Natural Science Foundation of Jiangsu Province","doi-asserted-by":"publisher","award":["BK2008206"],"award-info":[{"award-number":["BK2008206"]}],"id":[{"id":"10.13039\/501100004608","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Knowl. Discov. Data"],"published-print":{"date-parts":[[2009,7]]},"abstract":"<jats:p>\n            Clustering real-time stream data is an important and challenging problem. Existing algorithms such as CluStream are based on the\n            <jats:italic>k<\/jats:italic>\n            -means algorithm. These clustering algorithms have difficulties finding clusters of arbitrary shapes and handling outliers. Further, they require the knowledge of\n            <jats:italic>k<\/jats:italic>\n            and user-specified time window. To address these issues, this article proposes\n            <jats:italic>D-Stream<\/jats:italic>\n            , a framework for clustering stream data using a density-based approach.\n          <\/jats:p>\n          <jats:p>Our algorithm uses an online component that maps each input data record into a grid and an offline component that computes the grid density and clusters the grids based on the density. The algorithm adopts a density decaying technique to capture the dynamic changes of a data stream and a attraction-based mechanism to accurately generate cluster boundaries.<\/jats:p>\n          <jats:p>Exploiting the intricate relationships among the decay factor, attraction, data density, and cluster structure, our algorithm can efficiently and effectively generate and adjust the clusters in real time. Further, a theoretically sound technique is developed to detect and remove sporadic grids mapped by outliers in order to dramatically improve the space and time efficiency of the system. The technique makes high-speed data stream clustering feasible without degrading the clustering quality. The experimental results show that our algorithm has superior quality and efficiency, can find clusters of arbitrary shapes, and can accurately recognize the evolving behaviors of real-time data streams.<\/jats:p>","DOI":"10.1145\/1552303.1552305","type":"journal-article","created":{"date-parts":[[2009,7,28]],"date-time":"2009-07-28T12:43:55Z","timestamp":1248785035000},"page":"1-27","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":93,"title":["Stream data clustering based on grid density and attraction"],"prefix":"10.1145","volume":"3","author":[{"given":"Li","family":"Tu","sequence":"first","affiliation":[{"name":"Nanjing University of Aeronautics and Astronautics, Nanjing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yixin","family":"Chen","sequence":"additional","affiliation":[{"name":"Washington University in St. Louis, St. Louis, MO"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2009,7,28]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Proceedings of the International Conference on Very Large Databases (VLDB). 81--92","author":"Aggarwal C. C."},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2005.11.007"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/507515.507519"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.datak.2005.05.009"},{"key":"e_1_2_1_5_1","unstructured":"Chen Y. Dong G. Han J. Pei J. Wah B. W. and Wang J. 2002. OLAPing stream data: Is it feasible&amp;quest; In Proceedings of the Workshop on Research Issues in Data Mining and Knowledge Discovery ACM SIGMOD. 53--58.  Chen Y. Dong G. Han J. Pei J. Wah B. W. and Wang J. 2002. OLAPing stream data: Is it feasible&amp;quest; In Proceedings of the Workshop on Research Issues in Data Mining and Knowledge Discovery ACM SIGMOD. 53--58."},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/1281192.1281210"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2006.137"},{"key":"e_1_2_1_8_1","unstructured":"Giannella C. Han J. Pei J. Yan X. and Yu P. S. 2003. Mining frequent patterns in data streams at multiple-time granularities. Next Gen. Data Min. 191--212.  Giannella C. Han J. Pei J. Yan X. and Yu P. S. 2003. Mining frequent patterns in data streams at multiple-time granularities. Next Gen. Data Min. 191--212."},{"key":"e_1_2_1_9_1","volume-title":"Proceedings of the International Conference on Very Large Databases (VLDB).","author":"Gilbert A."},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/776985.776986"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2003.1198387"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.5555\/795666.796588"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2005.68"},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2005.10.021"},{"key":"e_1_2_1_15_1","volume-title":"Proceedings of the International Conference on Data Engineering (ICDE).","author":"O'Callaghan L."},{"key":"e_1_2_1_16_1","volume-title":"Proceedings of the 3rd ACIS International Conference on Software Engineering Research, Management and Applications.","author":"Oh S."},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1009745219419"},{"key":"e_1_2_1_18_1","volume-title":"Proceedings of the International Conference on Very Large Databases (VLDB).","author":"Subramaniam S."},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/RIDE.2005.8"},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/1150402.1150496"},{"key":"e_1_2_1_21_1","doi-asserted-by":"crossref","unstructured":"Wang Z. Wang B. Zhou C. and Xu X. 2004. Clustering data streams on the two-tier structure. Advanced Web Technologies and Applications 416--425.  Wang Z. Wang B. Zhou C. and Xu X. 2004. Clustering data streams on the two-tier structure. Advanced Web Technologies and Applications 416--425.","DOI":"10.1007\/978-3-540-24655-8_44"},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2005.06.008"}],"container-title":["ACM Transactions on Knowledge Discovery from Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/1552303.1552305","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/1552303.1552305","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T13:30:04Z","timestamp":1750253404000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/1552303.1552305"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2009,7]]},"references-count":22,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2009,7]]}},"alternative-id":["10.1145\/1552303.1552305"],"URL":"https:\/\/doi.org\/10.1145\/1552303.1552305","relation":{},"ISSN":["1556-4681","1556-472X"],"issn-type":[{"value":"1556-4681","type":"print"},{"value":"1556-472X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2009,7]]},"assertion":[{"value":"2008-06-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2008-12-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2009-07-28","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}