Maintaining frequent itemsets over high-speed data streams

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

Volumn 3918 LNAI, Issue , 2006, Pages 462-467

  Cheng, James ^a   Ke, Yiping ^a   Ng, Wilfred ^a  

^a HONG KONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Hong Kong)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; APPROXIMATION THEORY; COMPUTATIONAL COMPLEXITY; DATA ACQUISITION; DATA HANDLING; PROBLEM SOLVING;

FALSE-NEGATIVE APPROACH; FREQUENT ITEMSETS (FI); MINIMUM SUPPORT FUNCTION; SLIDING WINDOW;

DATABASE SYSTEMS;

EID: 33745785461     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11731139_53     Document Type: Conference Paper

References (6)

1. J. H. Chang and W. S. Lee. estWin: Adaptively Monitoring the Recent Change of Frequent Itemsets over Online Data Streams. In Proc. of CIKM, 2003.
2. J. H. Chang and W. S. Lee. A Sliding Window method for Finding Recently Frequent Itemsets over Online Data Streams. In Journal of Information Science and Engineering, Vol. 20, No. 4, July, 2004.
3. J. Cheng, Y. Ke, and W. Ng. Maintaining Frequent Itemsets over High-Speed Data Streams. Technical Report, http://www.cs.ust.hk/~csjames/pakdd06tr.pdf.
4. H. Li, S. Lee, and M. Shan. An Efficient Algorithm for Mining Frequent Itemsets over the Entire History of Data Streams. In Proc. of First International Workshop on Knowledge Discovery in Data Streams, 2004.
5. G. S. Manku and R. Motwani. Approximate Frequency Counts over Data Streams. In Proc. of VLDB, 2002.
6. J. Yu, Z. Chong, H. Lu, and A. Zhou. False Positive or False Negative: Mining Frequent Itemsets from High Speed Transactional Data Streams. In VLDB, 2004.