인터넷정보학회논문지 (Journal of Internet Computing and Services)

  • 제14권1호
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  • Pages.63-69
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  • 2013
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  • 1598-0170(pISSN)
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  • 2287-1136(eISSN)
한국인터넷정보학회 (Korean Society for Internet Information)
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확률 기법에 기반한 근접 빈발 패턴 마이닝 기법의 성능평가

Performance evaluation of approximate frequent pattern mining based on probabilistic technique

  • Pyun, Gwangbum (Dept. of Computer Science and Research Institute for Computer and Information Communication, Chungbuk National University) ;
  • Yun, Unil (Dept. of Computer Science and Research Institute for Computer and Information Communication, Chungbuk National University)
  • 투고 : 2012.10.31
  • 심사 : 2013.01.14
  • 발행 : 2013.02.28
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초록

근접 빈발 패턴 마이닝은 향상된 효율성을 위해 정확한 패턴보다 허용되는 범위 안에서 근접 빈발 패턴을 마이닝한다. 데이터베이스의 크기가 증대함에 따라 거대한 데이터베이스를 처리하기 위해서 더 빠른 마이닝 기법이 필요하게 되고 있다. 또한, 노이지나 데이터의 다양성 때문에 패턴을 마이닝 하는 것에 대한 정확한 결과를 찾기가 더 어렵다. 이러한 경우들에 대해, 근접 빈발 패턴 마이닝을 함으로 실행시간, 메모리 사용량, 그리고 확장성의 관점에서 더 효율적인 마이닝을 수행할 수 있다. 이 논문에서는 확률 기법에 근간한 근접 패턴 마이닝 알고리즘에 대한 특성을 살펴보고 척도가 되는 확률 기법에 기반한 근접 패턴 마이닝 알고리즘에 대해 성능 평가를 한다. 최종적으로 성능의 향상을 위해 테스트 결과를 분석한다.

Approximate Frequent pattern mining is to find approximate patterns, not exact frequent patterns with tolerable variations for more efficiency. As the size of database increases, much faster mining techniques are needed to deal with huge databases. Moreover, it is more difficult to discover exact results of mining patterns due to inherent noise or data diversity. In these cases, by mining approximate frequent patterns, more efficient mining can be performed in terms of runtime, memory usage and scalability. In this paper, we study the characteristics of an approximate mining algorithm based on probabilistic technique and run performance evaluation of the efficient approximate frequent pattern mining algorithm. Finally, we analyze the test results for more improvement.

키워드

참고문헌

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피인용 문헌

  1. Analysis and Performance Evaluation of Pattern Condensing Techniques used in Representative Pattern Mining vol.16, pp.2, 2015, https://doi.org/10.7472/jksii.2015.16.2.77
  2. Performance Analysis of Frequent Pattern Mining with Multiple Minimum Supports vol.14, pp.6, 2013, https://doi.org/10.7472/jksii.2013.14.6.01
  3. 슬라이딩 윈도우 기반의 스트림 하이 유틸리티 패턴 마이닝 기법 성능분석 vol.17, pp.6, 2013, https://doi.org/10.7472/jksii.2016.17.6.53