Sequential Pattern Mining Algorithms with Quantities

정량 정보를 포함한 순차 패턴 마이닝 알고리즘

  • 김철연 (서울대학교 전기컴퓨터공학부) ;
  • 임종화 (한국과학기술원 전산학과) ;
  • ;
  • 심규석 (서울대학교 전기컴퓨터공학부)
  • Published : 2006.10.15

Abstract

Discovering sequential patterns is an important problem for many applications. Existing algorithms find sequential patterns in the sense that only items are included in the patterns. However, for many applications, such as business and scientific applications, quantitative attributes are often recorded in the data, which are ignored by existing algorithms but can provide useful insight to the users. In this paper, we consider the problem of mining sequential patterns with quantities. We demonstrate that naive extensions to existing algorithms for sequential patterns are inefficient, as they may enumerate the search space blindly. Thus, we propose hash filtering and quantity sampling techniques that significantly improve the performance of the naive extensions. Experimental results confirm that compared with the naive extensions, these schemes not only improve the execution time substantially but also show better scalability for sequential patterns with quantities.

순차 패턴을 찾는 것은 데이타마이닝 응용분야에서 중요한 문제이다. 기존의 순차 패턴 마이닝 알고리즘들은 아이템으로만 이루어진 순차 패턴만을 취급하였으나 경제나 과학분야와 같은 많은 분야에서는 정량 정보가 아이템과 같이 기록되어 있으며, 기존의 알고리즘이 처리하지 못하는 이러한 정량 정보는 사용자에게 보다 유용한 정보를 전달하여 줄 수 있다. 본 논문에서는 정량 정보를 포함한 순차패턴 마이닝 문제를 제안하였다. 기존의 순차패턴 알고리즘에 대한 단순한 확장으로는 모든 정량에 대한 후보 패턴들을 모두 생성하기 때문에 확대된 탐색 공간을 효율적으로 탐색할 수 없음을 보이고, 이러한 단순한 확장 알고리즘의 성능을 대폭 향상시키기 위하여 정량 정보에 대해 해쉬 필터링과 정량 샘플링 기법을 제안하였다. 다양한 실험 결과들은 제안된 기법들이 단순히 확장된 알고리즘과 비교하여 수행시간을 매우 단축시켜 줄 뿐만 아니라, 데이타베이스 크기에 대한 확장성 또한 향상시켜줌을 보여 준다.

Keywords

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