Earticle

초록

영어

Frequent graph mining is one of famous data mining fields that receive the most attention, and its importance has been raised continually as recent databases in the real world become more complicated. Weighted frequent graph mining is an approach for applying importance of objects in the real world to the graph mining, and numerous studies related to this have been conducted so far. However, all of the results obtained from this approach do not become actually useful information, and a significant portion of them may be meaningless ones even though they are weighted frequent sub-graph patterns. To overcome this problem, in this paper, we propose a novel method which can consider whether any sub-graph pattern has close correlation among elements in the pattern, called MSCG (Mining Strongly Correlated sub-Graph). In experimental results, we demonstrate that our MSCG outperforms a state-of-the-art method with respect to runtime and memory usage.

목차

Abstract
 1. Introduction
 2. Background
  2.1. Related Work
  2.2. Preliminaries
 3. MSCG: Mining Strongly Correlated sub-Graph patterns
  3.1. Strongly Correlated Sub-graph
  3.2. Pruning Strategy for Weakly Correlated Sub-graphs
  3.3. MSCG Algorithm
 4. Analysis of Experimental Results
  4.1. Runtime Analysis
  4.2. Memory Usage Analysis
 5. Conclusions
 Acknowledgements
 References

키워드

저자

• Gangin Lee [ Department of Computer Science, Chungbuk National University, Republic of Korea ]
• Unil Yun [ Department of Computer Science, Chungbuk National University, Republic of Korea ] Corresponding author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Multimedia and Ubiquitous Engineering

• 간기

  월간

• pISSN

  1975-0080

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605