Journal of the Korea Institute of Information and Communication Engineering (한국정보통신학회논문지)

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
권호 표지
DOI QR코드

DOI QR Code

Discovery of Frequent Traversal Patterns from Weighted Traversals and Performance Enhancement by Traversal Split

가중치 순회로부터 빈발 순회패턴의 탐사 및 순회분할을 통한 성능향상

  • 이성대 (한국해양대학교 대학원 컴퓨터공학과) ;
  • 박휴찬 (한국해양대학교 IT공학부)
  • Published : 2007.05.31
Download PDF
⟨ Previous Next ⟩

Abstract

Many real world problems can be modeled as a graph and traversals on the graph. The structure of Web pages can be represented as a graph, for example, and user's navigation paths on the Web pages can be model as a traversal on the graph. It is interesting to discover valuable patterns, such as frequent patterns, from such traversals. In this paper, we propose an algorithm to discover frequent traversal patterns when a directed graph and weighted traversals on the graph are given. Furthermore, we propose a performance enhancement by traversal split and then verify it through experiments.

실세계의 많은 문제는 그래프와 그 그래프를 순회하는 트랜잭션으로 모델링 될 수 있다. 예를 들면, 웹페이지의 연결구조는 그래프로 표현될 수 있고, 사용자의 웹페이지 방문경로는 그 그래프를 순회하는 트랜잭션으로 모델링 될 수 있다. 이와 같이 그래프를 순회하는 트랜잭션들로부터 빈발 패턴과 같이 중요한 패턴을 찾아내는 것은 의미있는 일이다. 본 논문에서는, 방향 그래프와 그 그래프를 순회하는 가중치가 있는 트랜잭션들이 주어졌을 때, 빈발한 순회패턴을 탐사하는 알고리즘을 제안한다. 또한, 이 알고리즘의 성능향상을 위하여 순회를 분할하는 방법을 제안하고 실험을 통하여 검증한다.

Keywords

References

  1. R. Agawal and R. Srikant, Fast Algorithms for Mining Association Rules, Proc. of the 20th Int. Conf. on Very Large Database (VLDB), pp.487-499, Chile, Sep. 1994
  2. J. Han and M. Kamber, Data Mining Concepts and Techniques, Morgan Kaufman, 2001
  3. A. Nanopoulos and Y. Manolopoulos, 'Finding Generalized Path Patterns for Web Log Data Mining', Proc. of the 4th East-European Conf. on Advances in Databases and Information Systems (ADBIS), pp.215-225, Czech Republic, Sep. 2000
  4. A. Nanopoulos and Y. Manolopoulos, 'Mining Patterns from Graph Traversals', Data and Knowledge Engineering (DKE), vol. 37, no.3, pp.243-266, Jun. 2001 https://doi.org/10.1016/S0169-023X(01)00008-8
  5. M.S. Chen, J.S. Park, and P.S. Yu, 'Efficient Data Mining for Path Traversal Patterns', IEEE Trans. on Knowledge and Data Engineering, vol. 10, no.2, pp.209-221, Mar. 1998 https://doi.org/10.1109/69.683753
  6. J.S. Park, M.S. Chen, and P.S. Yu, 'An Effective Hash-Based Algorithm for Mining Association Rules', Proc. of ACM SIGMOD Int. Conf. of Management of Datap p.175-186, USA, May 1995
  7. A. Savasere, E. Omiecinski, and S.B. Navathe, 'An Efficient Algorithm for Mining Association Rules in Large Databases', Proc. of 21st Int. Conf. on Very Large Database (VLDB), pp.432-444, Switzerland, Sep. 1995
  8. R. Agrawal and R. Srikant, 'Mining Sequential Patterns', Proc. of International Conference on Data Engineering, pp.3-14, Taiwan, Mar. 1995
  9. C.H.Cai, W.e. Ada, W.e. Fu, C.H. Cheng, and W.W. Kwong, 'Mining Association Rules with Weighted Items', Proc.of International Database Engineering and Applications Symposium (IDEAS), pp.68-77, UK, Aug. 1998
  10. S.D. Lee and H.C. Park, 'Mining Frequent Patterns from Weighted Traversals on Graph using Confidence Interval and Pattern Priority', International Journal of Computer Science and Network Security (IJCSNS), vol. 6,no.5A,pp.136-141, May. 2006