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第12期 高兵等:基于共享最近邻密度的演化数据流聚类算法 ·1711· 参考文献 [10]Ren J,Ma R.Density-based data streams clustering over sliding [Babcock B,Babu S,Datar M,et al.Models and issues data windows /Proceedings of the 6th International Conference on stream systems /Proceedings of the 21st ACM SIGACTSIGMOD- Fusy systems and Knouledge Discorery.Tianjin,009:248 SIGART Symposium on Principles of Database Systems.Madison, [11]Wang H,Yu Y,Wang Q,et al.A density-based clustering 2002:1 structure mining algorithm for data streams /Proceedings of the 2]Guha S,Meyerson A,Mishra N,et al.Clustering data streams: 1st International Workshop on Big Data,Streams and Heterogene- theory and practice.IEEE Trans Knowl Data Eng,2003,15 (3): ous Source Mining:Algorithms,Systems,Programming Models 515 and Applications.New York:Association for Computing Machin- B]Zhang T,Ramakrishnan R.BIRCH:a efficient data clustering cy,2012:69 method for very large data bases /Proceedings of ACM SIGMOD [12]Chen Y,Tu L.Density-based clustering for realtime stream data Conference on Management of Data.Montreal,1996:103 /Proceedings of the 13th ACM SIGKDD International Conference 4]Ackermann M R,Martens M,Raupach C,et al.StreamKM++: on Knowledge Discovery and Data Mining.New York:Associa- a clustering algorithm for data streams.ACM I Exp Algorithmics, tion for Computing Machinery,2007:133 2012,17(2):article2.4 [13]Tu L,Chen Y.Stream data clustering based on grid density and 5]Aggarwal C C,Han J,Wang J,et al.A framework for clustering attraction.ACM Trans Knoul Discor Data,2009,3(3):12 evolving data streams /Proceedings of the 29th International Con- [14]Strehl A,Chosh J.A scalable approach to balanced,high-i- ference on Very Large Data Bases.Berlin,2003:81 mensional clustering of market-baskets /Proceedings of the 7th Aggarwal CC.Yu P S.A framework for clustering uncertain data International Conference on High Performance Computing.Berlin, streams.Proceedings of the 24th International Conference on 2000:525 Data Engineering.Cancun,2008:150 [15]Karypis G,Han E H,Kumar V.Chameleon:hierarchical cluste- ]Aggarwal C C.Han J,Wang J,et al.A framework for projected ring using dynamic modeling.Computer,1999,32(8):68 elustering of high dimensional data streams/Proceedings of the [16]Jarvis R A,Patrick E A.Clustering using a similarity measure Thirtieth International Conference on Very Large Data Bases.To- based on shared near neighbors.IEEE Trans Comput,1973,C- omto,2004:852 22(11):1025 [8]Ester M,Kriegel H P,Sander J.A density-ased algorithm for [17]Ertoz L,Steinbach M,Kumar V.Finding clusters of different si- discovering clusters in large spatial database with noise//Proceed- zes,shapes,and densities in noisy,high dimensional data / ings of the 2nd International Conference on Knowledge Discovering Proceedings of the 2003 SIAM International Conference on Data and Data Mining.Portland,1996:226 Mining.San Francisco,2003:47 9]Cao F,Ester M,Qian W,et al.Density-based clustering over an [18]Lihr S,Lazarescu M.Incremental clustering of dynamie data evolving data stream with noise /Proceedings of SIAM Conference streams using connectivity based representative points.Data on Data Mining.Bethesda,2006:326 Knoul Eng,2009,68:1第 12 期 高 兵等: 基于共享最近邻密度的演化数据流聚类算法 参 考 文 献 [1] Babcock B,Babu S,Datar M,et al. 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Clustering using a similarity measure based on shared near neighbors. IEEE Trans Comput,1973,C- 22( 11) : 1025 [17] Ertz L,Steinbach M,Kumar V. Finding clusters of different si￾zes,shapes,and densities in noisy,high dimensional data / / Proceedings of the 2003 SIAM International Conference on Data Mining. San Francisco,2003: 47 [18] Lühr S,Lazarescu M. Incremental clustering of dynamic data streams using connectivity based representative points. Data Knowl Eng,2009,68: 1 · 1171 ·
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