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Volumn 34, Issue 2, 2008, Pages 1178-1189

Discovering during-temporal patterns (DTPs) in large temporal databases

Author keywords

Data mining; During relationship; Temporal pattern

Indexed keywords

ALGORITHMS; DATABASE SYSTEMS; REAL TIME SYSTEMS; WEATHER FORECASTING;

EID: 36148947566     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2006.12.024     Document Type: Article
Times cited : (13)

References (14)
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    • Agrawal, R., Imielinski, T., & Swami, A. (1993). Mining association rules between sets of items in large databases. In Proceedings of the 1993 ACM SIGMOD international conference on management of data, Washington DC, May 26-28, 1993 (vol. 10, pp. 207-216).
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    • Maintaining knowledge about temporal intervals
    • Allen J.F. Maintaining knowledge about temporal intervals. Communication of the ACM 26 11 (1983) P832-P843
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  • 5
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    • Chen, G., Ai, J., & Yu, W. (2002). Discovering temporal association rules for time-lag data. In Proceedings of international conference on e-business (ICEB2002), Beijing, May 2002.
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    • Cheung, D. W., Han, J., Ng, V. T., & Wong, C. Y. (1996). Maintenance of discovered association rules in large databases: an incremental updating technique. In S. Y. W. Su, (Ed.), Proceedings of the international conference on data engineering (ICDE'96) (pp. 106-114).
  • 7
    • 36148992517 scopus 로고    scopus 로고
    • Cheung, D. W., Lee, S. D., & Kao, B. (1997). A general incremental technique for maintaining discovered association rules. In Proceedings of the fifth international conference on database systems for advanced applications.
  • 8
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    • Chi, Y., Wang, H. J., Yu, P. S., & Muntz, R. R. (2004). Catch the moment maintaining closed frequent itemsets. http://citeseer.ist.psu.edu.
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    • Hoppner, F. (2001). Discovery of temporal patterns-learning rules about the qualitative behaviour of time series. In PKDD'01, number 2168 of LNAI, Freiburg, Germany (pp. 192-203).
  • 11
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    • Jin, C., Qian, W., Sha, C., Yu, J., & Zhou, A. (2003). Dynamically maintaining frequent items over a data stream. In Proceedings of the 12th ACM CIKM international conference on information and knowledge management (pp. 287-294).
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    • Rainsford, C. P., & Roddic, J. F. (1999). Adding temporal semantics to association rules. In Proceedings of the 3rd European conference on principles and practice of knowledge discovery in databases (pp. 504-509).
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    • Veloso, A., Meira, W., Jr., De Carvalho, M. B., Parthasarathy, S., & Zaki, M. (2003). Parallel, incremental and interactive mining for frequent itemsets in evolving databases. In: Proceedings of the sixth SIAM workshop on high performance data mining, May 2003.
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    • SPADE: an efficient algorithm for mining frequent sequences
    • Zaki M.J. SPADE: an efficient algorithm for mining frequent sequences. Machine Learning 42 1/2 (2000) 31-60
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    • Zaki, M.J.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.