Journal of the Korea Society of Computer and Information (한국컴퓨터정보학회논문지)

Korean Society of Computer Information (한국컴퓨터정보학회)
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Improved approach of calculating the same shape in graph mining

그래프 마이닝에서 그래프 동형판단연산의 향상기법

  • 노영상 (충북대학교, 컴퓨터공학) ;
  • 윤은일 (충북대학교 전자정보대학 컴퓨터) ;
  • 김명준 (충북대학교 전자정보대학 컴퓨터)
  • Published : 2009.10.31
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Abstract

Data mining is a method that extract useful knowledges from huge size of data. Recently, a focussing research part of data mining is to find interesting patterns in graph databases. More efficient methods have been proposed in graph mining. However, graph analysis methods are in NP-hard problem. Graph pattern mining based on pattern growth method is to find complete set of patterns satisfying certain property through extending graph pattern edge by edge with avoiding generation of duplicated patterns. This paper suggests an efficient approach of reducing computing time of pattern growth method through pattern growth's property that similar patterns cause similar tasks. we suggest pruning methods which reduce search space. Based on extensive performance study, we discuss the results and the future works.

그래프마이닝에서 그래프패턴의 동형판단문제는 지수함수적 계산시간을 요구하기 때문에 그래프마이닝의 전체수행시간에서 동형판단 연산이 차지하는 비율이 매우 높다. 그러므로 그래프마이닝 알고리즘은 그래프동형판단을 최대한 효율적으로 할 필요가 있다. 본 논문은 그래프마이닝에서 빠른 수행시간을 보이는 gaston 알고리즘의 동형판단효율성을 증가시켜 수행시간을 평가해 보았으며, 제시한 방법으로 인해 더욱 향상된 성능을 보인다.

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References

  1. R. Agrawal. T. Imilienski. and A. Swami, "Mining association rules between sets of items in large datasets." In Proceedings of SIGMOD 1993.
  2. T. Asai, K Abe, S. Kawasoe, H. Arimura. H. Sakamoto, S. Arikawa "Efficient substructure discovery from large semi-structured data." In Proceedings of KDD'04 SIAM SDM'02, April 2002,
  3. J. Han and M. Kamber. "Data Mining: Concepts and Techniques." Morgan Kaufmann. Publishers, 2005.
  4. J. Huan, W. Wang, Jan Prins "Efficient mining of frequent subgraphs in the presence of isomorphism." In Proceedings of the Third IEEE International Conference on Data Mining (ICDM'03)
  5. J. Huan, W. Wang, J. Prins, J. Yang "SPIN: mining maximal frequent subgraphs from graph databases," KDD'04: In Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
  6. A. Inokuchi. Takashi Washio, Hiroshi Motoda "An apriori-based algorithm for mining frequent substructures from graph data." In Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Disco (2000)
  7. M. Kuramoehi and G. Karypis. "An Efficient Algorithm for Discovering Frequent Subgraphs," In Proceedings of IEEE Trans. Knowl. Data Eng. 16(9): 1038-1051. 2004. https://doi.org/10.1109/TKDE.2004.33
  8. S. Niissen. J, N. Kok "A quickstart in frequent structure mining can make a difference," In Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining (KDD'04)
  9. N. Vanetik, E. Gudes. "Mining Frequent Labeled and Partially Labeled Graph Patterns," In Proceedings of the International Conference on Data Engineering 2004 (ICDE2004). 2004.
  10. X. Yan, J. Han "gSpan: Graph-based substructure pattern mining." In Proceedings of the 2002 IEEE International Conference on Data Mining
  11. X. Yan, J. Han "CloseGraph: Mining Closed Frequent Graph Patterns," In Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, 2003.
  12. F. Zhu, X. Yan, J. Han, P. S. Yu "gPrune: A Constraint Pushing Framework for Graph Pattern Mining,H In Proceedings of Pacific-Asia Conf. on Knowledge Discovery and Data Mining (PAKDD' 07)
  13. 산사볼트가람라흐차, 황영섭, "근사 알고리즘을 이용한 순차패턴 탐색." 한국컴퓨터정보학회 논문지, 제 14권, 제 5호, 29-36쪽, 2009년 5월.
  14. 오승준, "데이터마이닝의 자동 데이터 규칙 추출 방법론 개발: 계층적 클러스터링 알고리듬과 러프 셋 이론을 중심으로." 한국컴퓨터정보학회 논문지, 제 14권, 제 6호, 135-142쪽, 2009년 6월.