An EM-based algorithm for clustering data streams in sliding windows

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5463, Issue , 2009, Pages 230-235

  Dang, Xuan Hong ^a   Lee, Vincent ^a   Ng, Wee Keong ^b   Ciptadi, Arridhana ^b   Ong, Kok Leong ^c  

^a MONASH UNIVERSITY   (Australia)

^b NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

^c DEAKIN UNIVERSITY   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

CLUSTERING DATA; DATA MINING TASKS; DATA STREAM; DATA UNDERSTANDING; EXPECTATION MAXIMIZATION; MINING SYSTEMS; ONE-PASS; SLIDING WINDOW;

CLUSTERING ALGORITHMS; DATA COMMUNICATION SYSTEMS; DATA MINING; DATABASE SYSTEMS; DECODING; MINING; WINDOWS;

CLUSTER ANALYSIS;

EID: 67650162774     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-00887-0_18     Document Type: Conference Paper

References (5)

1. Aggarwal, C.C., Han, J., Wang, J., Yu, P.S.: A framework for clustering evolving data streams. In: Aberer, K., Koubarakis, M., Kalogeraki, V. (eds.) VLDB 2003. LNCS, vol. 2944, pp. 81-92. Springer, Heidelberg (2004)
2. Babcock, B., Babu, S., Datar, M., Motwani, R., Widom, J.: Models and issues in data stream systems. In: PODS, pp. 1-16 (2002) (Pubitemid 35017047)
3. Cao, F., Ester, M., Qian, W., Zhou, A.: Density-based clustering over an evolving data stream with noise. In: Jonker, W., Petković, M. (eds.) SDM 2006. LNCS, vol. 4165. Springer, Heidelberg (2006)
4. Chen, Y., Tu, L.: Density-based clustering for real-time stream data. In: SIGKDD Conference, pp. 133-142 (2007) (Pubitemid 350229200)
5. Aoying, Z., Feng, C., Ying, Y., Chaofeng, S., Xiaofeng, H.: Distributed data stream clustering: A fast em-based approach. In: ICDE Conference, pp. 736-745 (2007)