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      KCI등재 SCOPUS

      Contribution to Improve Database Classification Algorithms for Multi-Database Mining

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      https://www.riss.kr/link?id=A105428582

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      다국어 초록 (Multilingual Abstract)

      Database classification is an important preprocessing step for the multi-database mining (MDM). In fact,when a multi-branch company needs to explore its distributed data for decision making, it is imperative toclassify these multiple databases into si...

      Database classification is an important preprocessing step for the multi-database mining (MDM). In fact,when a multi-branch company needs to explore its distributed data for decision making, it is imperative toclassify these multiple databases into similar clusters before analyzing the data. To search for the bestclassification of a set of n databases, existing algorithms generate from 1 to (n2–n)/2 candidate classifications.
      Although each candidate classification is included in the next one (i.e., clusters in the current classification aresubsets of clusters in the next classification), existing algorithms generate each classification independently,that is, without taking into account the use of clusters from the previous classification. Consequently, existingalgorithms are time consuming, especially when the number of candidate classifications increases. Toovercome the latter problem, we propose in this paper an efficient approach that represents the problem ofclassifying the multiple databases as a problem of identifying the connected components of an undirectedweighted graph. Theoretical analysis and experiments on public databases confirm the efficiency of ouralgorithm against existing works and that it overcomes the problem of increase in the execution time.

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      참고문헌 (Reference)

      1 H. Liu, "Toward multi-database mining: identifying relevant databases" 13 (13): 541-553, 2001

      2 R. Agrawal, "Parallel mining of association rules" 8 (8): 962-969, 1996

      3 S. Zhang, "Multi-database mining" 2 (2): 5-13, 2003

      4 S. Zhang, "Mining multiple data sources: local pattern analysis" 12 (12): 121-125, 2006

      5 J. Han, "Mining frequent patterns without candidate generation: a frequent-pattern tree approach" 8 (8): 53-87, 2004

      6 S. Zhang, "Knowledge Discovery in Multiple Databases" Springer 2004

      7 T. H. Cormen, "Introduction to Algorithms" MIT Press 1990

      8 H. Liu, "Identifying relevant databases for multidatabase mining" 15-18, 1998

      9 R. Agrawal, "Fast algorithms for mining association rules in large databases" 487-499, 1994

      10 A. Adhikari, "Efficient clustering of databases induced by local patterns" 44 (44): 925-943, 2008

      1 H. Liu, "Toward multi-database mining: identifying relevant databases" 13 (13): 541-553, 2001

      2 R. Agrawal, "Parallel mining of association rules" 8 (8): 962-969, 1996

      3 S. Zhang, "Multi-database mining" 2 (2): 5-13, 2003

      4 S. Zhang, "Mining multiple data sources: local pattern analysis" 12 (12): 121-125, 2006

      5 J. Han, "Mining frequent patterns without candidate generation: a frequent-pattern tree approach" 8 (8): 53-87, 2004

      6 S. Zhang, "Knowledge Discovery in Multiple Databases" Springer 2004

      7 T. H. Cormen, "Introduction to Algorithms" MIT Press 1990

      8 H. Liu, "Identifying relevant databases for multidatabase mining" 15-18, 1998

      9 R. Agrawal, "Fast algorithms for mining association rules in large databases" 487-499, 1994

      10 A. Adhikari, "Efficient clustering of databases induced by local patterns" 44 (44): 925-943, 2008

      11 A. Adhikari, "Developing Multi-database Mining Applications" Springer 2010

      12 X. Wu, "Database classification for multi-database mining" 30 (30): 71-88, 2005

      13 Y. Liu, "Completely clustering for multi-databases mining" 9 (9): 6595-6602, 2013

      14 H. Li, "An improved database classification algorithm for multi-database mining" 346-357, 2009

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      학술지 이력

      학술지 이력
      연월일 이력구분 이력상세 등재구분
      2023 평가예정 해외DB학술지평가 신청대상 (해외등재 학술지 평가)
      2020-01-01 평가 등재학술지 유지 (해외등재 학술지 평가) KCI등재
      2012-01-01 평가 등재학술지 선정 (등재후보2차) KCI등재
      2011-01-01 평가 등재후보 1차 PASS (등재후보1차) KCI등재후보
      2009-01-01 평가 등재후보학술지 선정 (신규평가) KCI등재후보
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      학술지 인용정보

      학술지 인용정보
      기준연도 WOS-KCI 통합IF(2년) KCIF(2년) KCIF(3년)
      2016 0.09 0.09 0.09
      KCIF(4년) KCIF(5년) 중심성지수(3년) 즉시성지수
      0.07 0.06 0.254 0.59
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