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Volumn 19, Issue 10, 2008, Pages 2585-2596

Mining the frequent patterns in an arbitrary sliding window over online data streams

Author keywords

Data stream; Frequent pattern mining; Sliding window; Time decaying model

Indexed keywords

DATA MINING; DECODING; KNOWLEDGE BASED SYSTEMS; RIVERS; STREAM FLOW; WINDOWS;

EID: 55149125179     PISSN: 10009825     EISSN: None     Source Type: Journal    
DOI: 10.3724/SP.J.1001.2008.02585     Document Type: Article
Times cited : (33)

References (14)
  • 2
    • 33644920942 scopus 로고    scopus 로고
    • Research issues in data stream association rule mining
    • Jiang N, Gruenwald L. Research issues in data stream association rule mining. ACM SIGMOD Record, 2006, 35(1): 14-19.
    • (2006) ACM SIGMOD Record , vol.35 , Issue.1 , pp. 14-19
    • Jiang, N.1    Gruenwald, L.2
  • 3
    • 0036367429 scopus 로고    scopus 로고
    • Querying and mining data streams: You only get one look a tutorial
    • Franklin M.J., Moon B. and Ailamaki A.(ed.), Madison: ACM Press
    • Garofalakis MN, Gehrke J. Querying and mining data streams: You only get one look a tutorial. In: Franklin MJ, Moon B, Ailamaki A, eds. Proc. of the 2002 ACM SIGMOD Int'l Conf. on Management of Data. Madison: ACM Press, 2002. 635-635.
    • (2002) Proc. of the 2002 ACM SIGMOD Int'l Conf. on Management of Data , pp. 635-635
    • Garofalakis, M.N.1    Gehrke, J.2
  • 5
    • 77952414052 scopus 로고    scopus 로고
    • Finding recent frequent itemsets adaptively over online data streams
    • Lise G., Ted E.S., Pedro D. and Christos F.(ed.), Washington: ACM Press
    • Chang JH, Lee WS. Finding recent frequent itemsets adaptively over online data streams. In: Lise G, Ted ES, Pedro D, Christos F, eds. Proc. of the 9th ACM SIGKDD Int'l Conf. on Knowledge Discovery and Data Mining. Washington: ACM Press, 2003. 487-492.
    • (2003) Proc. of the 9th ACM SIGKDD Int'l Conf. on Knowledge Discovery and Data Mining , pp. 487-492
    • Chang, J.H.1    Lee, W.S.2
  • 6
    • 33749554404 scopus 로고    scopus 로고
    • CFI-Stream: Mining closed frequent itemsets in data streams
    • Roberto B., Kristin P.B., Gautam D., Dimitrios G. and Johannes G.(ed.), Philadelphia: ACM Press
    • Jiang N, Gruenwald L. CFI-Stream: Mining closed frequent itemsets in data streams. In: Roberto B, Kristin PB, Gautam D, Dimitrios G, Johannes G, eds. Proc. of the 12th ACM SIGKDD Int'l Conf. on Knowledge Discovery and Data Mining. Philadelphia: ACM Press, 2006. 592-597.
    • (2006) Proc. of the 12th ACM SIGKDD Int'l Conf. on Knowledge Discovery and Data Mining , pp. 592-597
    • Jiang, N.1    Gruenwald, L.2
  • 7
    • 33646140552 scopus 로고    scopus 로고
    • A false negative approach to mining frequent itemsets from high speed transactional data streams
    • Yu JX, Chong Z, Lu H, Zhang Z, Zhou A. A false negative approach to mining frequent itemsets from high speed transactional data streams. Information Sciences, 2006, 176(4): 1986-2015.
    • (2006) Information Sciences , vol.176 , Issue.4 , pp. 1986-2015
    • Yu, J.X.1    Chong, Z.2    Lu, H.3    Zhang, Z.4    Zhou, A.5
  • 8
    • 84878048755 scopus 로고    scopus 로고
    • DStree: A tree structure for the mining of frequent sets from data streams
    • Clifton C.W., Zhong N., Liu J.M., Wah B.W.and Wu X.D.(ed.), Hong Kong: IEEE Press
    • Leung CKS, Khan QI. DStree: A tree structure for the mining of frequent sets from data streams. In: Clifton CW, Zhong N, Liu JM, Wah BW, Wu XD, eds. Proc. of the 6th Int'l Conf. on Data Mining. Hong Kong: IEEE Press, 2006. 928-932.
    • (2006) Proc. of the 6th Int'l Conf. on Data Mining , pp. 928-932
    • Leung, C.K.S.1    Khan, Q.I.2
  • 10
    • 34250753732 scopus 로고    scopus 로고
    • Optimal multi-scale patterns in time series streams
    • Roberto B., Kristin P.B., Gautam D., Dimitrios G. and Johannes G.(ed.), Chicago: ACM Press
    • Papadimitriou A, Yu PS. Optimal multi-scale patterns in time series streams. In: Roberto B, Kristin PB, Gautam D, Dimitrios G, Johannes G, eds. Proc. of the 2006 ACM SIGMOD Int'l Conf. of Management of Data. Chicago: ACM Press, 2006. 647-658.
    • (2006) Proc. of the 2006 ACM SIGMOD Int'l Conf. of Management of Data , pp. 647-658
    • Papadimitriou, A.1    Yu, P.S.2
  • 12
    • 55149092672 scopus 로고    scopus 로고
    • Incremental mining of sequential patterns over a stream sliding window
    • Tsumto S., Clifton C.W., Zhong N., Wu X.D., Liu J.M., Wah B.W. and Cheung Y.M.(ed.), Hong Kong: IEEE Press
    • Ho CC, Li HF, Kuo FF, Lee SY. Incremental mining of sequential patterns over a stream sliding window. In: Tsumto S, Clifton CW, Zhong N, Wu XD, Liu JM, Wah BW, Cheung YM, eds. Proc. of the 6th Int'l Conf. on Data Mining Workshops. Hong Kong: IEEE Press, 2006. 677-681.
    • (2006) Proc. of the 6th Int'l Conf. on Data Mining Workshops , pp. 677-681
    • Ho, C.C.1    Li, H.F.2    Kuo, F.F.3    Lee, S.Y.4
  • 13
    • 34250676594 scopus 로고    scopus 로고
    • A simpler and more efficient deterministic scheme for finding frequent items over sliding windows
    • Roberto B., Kristin P.B., Gautam D., Dimitrios G. and Johannes G.(ed.), Chicago: ACM Press
    • Lee L, Ting H. A simpler and more efficient deterministic scheme for finding frequent items over sliding windows. In: Roberto B, Kristin PB, Gautam D, Dimitrios G, Johannes G, eds. Proc. of the 25th ACM SIGMOD-SIGACT-SIGART Symp. on Principles of Database Systems. Chicago: ACM Press, 2006. 290-297.
    • (2006) Proc. of the 25th ACM SIGMOD-SIGACT-SIGART Symp. on Principles of Database Systems , pp. 290-297
    • Lee, L.1    Ting, H.2
  • 14
    • 0039253846 scopus 로고    scopus 로고
    • Mining frequent patterns without candidate generation
    • Chen W., Naughton J.F. and Bernstein P.A.(ed.), Dallas: ACM Press
    • Han J, Pei J, Yin Y. Mining frequent patterns without candidate generation. In: Chen W, Naughton JF, Bernstein PA, eds. Proc. of the 2000 ACM SIGMOD Int'l Conf. on Management of Data. Dallas: ACM Press, 2000. 1-12.
    • (2000) Proc. of the 2000 ACM SIGMOD Int'l Conf. on Management of Data , pp. 1-12
    • Han, J.1    Pei, J.2    Yin, Y.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.