{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T15:32:18Z","timestamp":1773156738749,"version":"3.50.1"},"reference-count":17,"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"}],"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>In data stream clustering, it is desirable to have algorithms that are able to detect clusters of arbitrary shape, clusters that evolve over time, and clusters with noise. Existing stream data clustering algorithms are generally based on an online-offline approach: The online component captures synopsis information from the data stream (thus, overcoming real-time and memory constraints) and the offline component generates clusters using the stored synopsis. The online-offline approach affects the overall performance of stream data clustering in various ways: the ease of deriving synopsis from streaming data; the complexity of data structure for storing and managing synopsis; and the frequency at which the offline component is used to generate clusters. In this article, we propose an algorithm that (1) computes and updates synopsis information in constant time; (2) allows users to discover clusters at multiple resolutions; (3) determines the right time for users to generate clusters from the synopsis information; (4) generates clusters of higher purity than existing algorithms; and (5) determines the right threshold function for density-based clustering based on the fading model of stream data. To the best of our knowledge, no existing data stream algorithms has all of these features. Experimental results show that our algorithm is able to detect arbitrarily shaped, evolving clusters with high quality.<\/jats:p>","DOI":"10.1145\/1552303.1552307","type":"journal-article","created":{"date-parts":[[2009,7,28]],"date-time":"2009-07-28T12:43:55Z","timestamp":1248785035000},"page":"1-28","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":98,"title":["Density-based clustering of data streams at multiple resolutions"],"prefix":"10.1145","volume":"3","author":[{"given":"Li","family":"Wan","sequence":"first","affiliation":[{"name":"Nanyang Technological Unviserity"}]},{"given":"Wee Keong","family":"Ng","sequence":"additional","affiliation":[{"name":"Nanyang Technological Unviserity"}]},{"given":"Xuan Hong","family":"Dang","sequence":"additional","affiliation":[{"name":"Institute of Infocomm Research, Singapore"}]},{"given":"Philip S.","family":"Yu","sequence":"additional","affiliation":[{"name":"University of Illinios at Chicago"}]},{"given":"Kuan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Singapore Management University"}]}],"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. 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