Browse > Article
http://dx.doi.org/10.3745/KIPSTD.2005.12D.3.335

A Sliding Window Technique for Open Data Mining over Data Streams  

Chang Joong-Hyuk (연세대학교 대학원 컴퓨터과학과)
Lee Won-Suk (연세대학교 컴퓨터과학과)
Publication Information
The KIPS Transactions:PartD / v.12D, no.3, 2005 , pp. 335-344 More about this Journal
Abstract
Recently open data mining methods focusing on a data stream that is a massive unbounded sequence of data elements continuously generated at a rapid rate are proposed actively. Knowledge embedded in a data stream is likely to be changed over time. Therefore, identifying the recent change of the knowledge quickly can provide valuable information for the analysis of the data stream. This paper proposes a sliding window technique for finding recently frequent itemsets, which is applied efficiently in open data mining. In the proposed technique, its memory usage is kept in a small space by delayed-insertion and pruning operations, and its mining result can be found in a short time since the data elements within its target range are not traversed repeatedly. Moreover, the proposed technique focused in the recent data elements, so that it can catch out the recent change of the data stream.
Keywords
Sliding Window Technique; Open Data Mining; Changeability of a Data Stream; Data Stream; Real-time Analysis;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 장중혁, 이원석. 데이터 스트림에서 개방 데이터 마이닝 기반의 빈발항목 탐색. 정보처리학회논문지D, 10-D(3), 2003   과학기술학회마을
2 M. Garofalakis, J. Gehrke, and R. Rastogi, Querying and Mining Data Streams: You Only Get One Look, The tutorial notes of the 28th Int'l Conference on Very on Large Data Bases, 2002   DOI
3 C. Hidber, Online association rule mining, Proceedings of the ACM SIGMOD International Conference on Management of Data, Philadelphia, PA, pp.145-156, May, 1999   DOI
4 M. Charikar, K. Chen, M. Farach-Colton, Finding frequent items in data streams, Proceedings of the 29th International Colloquium on Automata, Language and Programming, 2002
5 D. Lambert, J.C. Pinheiro, Mining a stream of transactions for customer patterns, Proceedings of the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.305-310, 2001   DOI
6 R. Agrawal, R. Srikant, Fast algorithms for mining association rules, Proceedings of the 20th International Conference on Very Large Databases, Santiago, Chile, September, 1994
7 G. Hulten, L. Spencer, P. Domingos, Mining time-changing data streams, Proceedings of the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.97-106, 2001   DOI
8 S. Brin, R. Motwani, J.D. Ullman, S. Tsur, Dynamic itemset counting and implication rules for market basket data, Proceedings of the ACM SIGMOD International Conference on Management of Data, Tucson, AZ, pp.255-264, May, 1997   DOI
9 R.C. Agarwal, C.C. Aggarwal, V.V.V. Prasad, Depth first generation of long patterns, Proceedings of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Boston, MA, pp.108-118, September, 2000   DOI
10 A. Savasers, E. Omiecinski, S. Navathe, An efficient algorithm for mining association rules in large databases, Proceedings of the 21st International Conference on Very Large Databases, pp.432-444, 1995
11 G.S. Manku, R. Motwani, Approximate frequency counts over data streams, Proceedings of the 28th International Conference on Very Large Databases, Hong Kong, China, August, 2002
12 M. Datar, A. Gionis, P. Indyk, R. Motawi, Maintaining stream statistics over sliding windows, Proceedings of the 13th Annual ACM-SIAM Symposium on Discrete Algorithms, January, 2002
13 C.-H. Lee, C.-R. Lin, M.-S. Chen, Sliding-window filtering: An efficient algorithm for incremental mining, Proceedings of the 10th International Conference on Information and Knowledge Management, Atlanta, GE, pp.263-270, November, 2001   DOI