A framework for clustering massive text and categorical data streams

Proceedings of the Sixth SIAM International Conference on Data Mining

Volumn 2006, Issue , 2006, Pages 479-483

  Aggarwal, Charu C ^a   Yu, Philip S ^a  

^a IBM T J WATSON RESEARCH CENTER   (United States)

Author keywords

Categorical data; Clustering; Streams; Text

Indexed keywords

COMPUTER APPLICATIONS; COMPUTER SIMULATION; DATA RECORDING; IMAGE SEGMENTATION; INFORMATION RETRIEVAL SYSTEMS; PROBLEM SOLVING; REAL TIME SYSTEMS; STATISTICAL METHODS;

CATEGORICAL DATA; CLUSTERING; DOCUMENT ORGANIZATION; STREAMS;

DATA MINING;

EID: 33745442151     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1137/1.9781611972764.44     Document Type: Conference Paper

References (4)

1. C. C. Aggarwal, A Framework for Diagnosing Changes in Evolving Data Streams, ACM SIGMOD Conference, (2003), pp. 575-586.
2. C. C. Aggarwal, J. Han, J. Wang, and P. Yu, A Framework for Clustering Evolving Data Streams, VLDB Conference, (2003), pp. 81-92.
3. L. O'Callaghan, N. Mishra, A. Meyerson, S. Guha, and R. Motwani, Streaming-Data Algorithms For High-Quality Clustering, ICDE Conference, (2002), pp. 685-696.
4. T. Zhang, R. Ramakrishnan, and M. Livny, BIRCH: An Efficient Data Clustering Method for Very Large Databases, ACM SIGMOD Conference, (1996), pp. 103-114.