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Volumn 2798, Issue , 2003, Pages 253-265

Efficient monitoring of patterns in data mining environments

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPUTERS;

EID: 33745767871     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-39403-7_20     Document Type: Article
Times cited : (30)

References (16)
  • 3
    • 23144437564 scopus 로고    scopus 로고
    • Monitoring the Results of the KDD Process: An Overview of Pattern Evolution
    • J. Meij, editor, chapter 6. STT Netherlands Study Center for Technology Trends, The Hague, Netherlands, Apr.
    • S. Baron and M. Spiliopoulou. Monitoring the Results of the KDD Process: An Overview of Pattern Evolution. In J. Meij, editor, Dealing with the data flood: mining data, text and multimedia, chapter 6. STT Netherlands Study Center for Technology Trends, The Hague, Netherlands, Apr. 2002.
    • (2002) Dealing with the Data Flood: Mining Data, Text and Multimedia
    • Baron, S.1    Spiliopoulou, M.2
  • 5
    • 78149338936 scopus 로고    scopus 로고
    • Analyzing the interestingness of association rules from the temporal dimension
    • Silicon Valley, USA, November
    • Y. M. Bing Liu and R. Lee. Analyzing the interestingness of association rules from the temporal dimension. In IEEE International Conference on Data Mining (ICDM-2001), pages 377-384, Silicon Valley, USA, November 2001.
    • (2001) IEEE International Conference on Data Mining (ICDM-2001) , pp. 377-384
    • Bing Liu, Y.M.1    Lee, R.2
  • 6
    • 0002758989 scopus 로고    scopus 로고
    • Mining Surprising Patterns Using Temporal Description Length
    • A. Gupta, O. Shmueli, and J. Widom, editors, New York City, NY, Aug. Morgan Kaufmann
    • S. Chakrabarti, S. Sarawagi, and B. Dom. Mining Surprising Patterns Using Temporal Description Length. In A. Gupta, O. Shmueli, and J. Widom, editors, VLDB'98, pages 606-617, New York City, NY, Aug. 1998. Morgan Kaufmann.
    • (1998) VLDB'98 , pp. 606-617
    • Chakrabarti, S.1    Sarawagi, S.2    Dom, B.3
  • 7
    • 84956869225 scopus 로고    scopus 로고
    • Mining Temporal Features in Association Rules
    • Proceedings of the 3rd European Conference on Principles of Data Mining and Knowledge Discovery, Prague, Czech Republic, September Springer
    • X. Chen and I. Petrounias. Mining Temporal Features in Association Rules. In Proceedings of the 3rd European Conference on Principles of Data Mining and Knowledge Discovery, Lecture Notes in Computer Science, pages 295-300, Prague, Czech Republic, September 1999. Springer.
    • (1999) Lecture Notes in Computer Science , pp. 295-300
    • Chen, X.1    Petrounias, I.2
  • 8
    • 0001666210 scopus 로고    scopus 로고
    • A General Incremental Technique for Maintaining Discovered Association Rules
    • Melbourne, Australia, Apr.
    • D. W. Cheung, S. Lee, and B. Kao. A General Incremental Technique for Maintaining Discovered Association Rules. In DASFAA'97, Melbourne, Australia, Apr. 1997.
    • (1997) DASFAA'97
    • Cheung, D.W.1    Lee, S.2    Kao, B.3
  • 16
    • 0031188758 scopus 로고    scopus 로고
    • Discovering Patterns from Large and Dynamic Sequential Data
    • K. Wang. Discovering Patterns from Large and Dynamic Sequential Data. Intelligent Information Systems, 9:8-33, 1997.
    • (1997) Intelligent Information Systems , vol.9 , pp. 8-33
    • Wang, K.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.