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Volumn 17, Issue 6, 2005, Pages 774-785

SMCA: A general model for mining asynchronous periodic patterns in temporal databases

Author keywords

Asynchronous sequence; Partial periodicity; Periodic pattern; Temporal database

Indexed keywords

ALGORITHMS; COMPUTATIONAL COMPLEXITY; DATA PROCESSING; DATABASE SYSTEMS; PATTERN RECOGNITION; PROTEINS; WORLD WIDE WEB;

EID: 20844447400     PISSN: 10414347     EISSN: None     Source Type: Journal    
DOI: 10.1109/TKDE.2005.98     Document Type: Article
Times cited : (57)

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  • 9
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    • Mining partially periodic event patterns with unknown periods
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    • Ma, S.1    Hellerstein, J.2
  • 15
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    • Efficient mining of intertransaction association rules
    • A.K.H. Tung, H. Lu, J. Han, and L. Feng, "Efficient Mining of Intertransaction Association Rules," IEEE Trans. Knowledge and Data Eng., vol. 15, no. 1, pp. 43-56, 2003.
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    • Mining asynchronous periodic patterns in time series data
    • J. Yang, W. Wang, and P.S. Yu, "Mining Asynchronous Periodic Patterns in Time Series Data," IEEE Trans. Knowledge and Data Eng., vol. 15, no. 3, pp. 613-628, 2003.
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    • Yang, J.1    Wang, W.2    Yu, P.S.3
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    • Spade: An efficient algorithm for mining frequent sequences
    • M.J. Zaki, "Spade: An Efficient Algorithm for Mining Frequent Sequences," Machine Learning, vol. 42, nos. 1/2, pp. 31-60, 2001.
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    • Zaki, M.J.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.