Earticle

초록

영어

Association rule mining is now widely used in many fields such as commerce, telecom, insurance, and bioinformatics. Although association rule mining has improved in performance, the real commerce database has also grown in its size and dimension to a point of creating millions of association rules. One of the biggest problems of association rule mining is that it frequently produces large numbers of rules, and this makes it difficult for users to select those that are of interest. We proposed the Common Sense Measure (CSM) so that only interesting knowledge can be selected in order to resolve the problem resulting from a large quantity of rules. The CSM is an interestingness measure that evaluates how closely rules match the common sense knowledge. We developed an algorithm of rule matching method with common sense knowledge using the common sense network (CSN).

목차

Abstract
 1. Introduction
 2. Related Works
 3. Common Sense Knowledge Based Interestingness Measure
 4. CSM Implementation
  4.1. The Knowledge Component Vector
  4.2. Semantic Network Based Rule Matching Method with Common Sense Knowledge
  4.3. CSM Calculation
 5. Experiment
 6. Conclusion
 Acknowledgements
 References

키워드

저자

• Ingi Lee [ Computer Science Engineering, Ewha Womans University ]
• Hwan-Seung Yong [ Computer Science Engineering, Ewha Womans University ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Bio-Science and Bio-Technology

• 간기

  격월간

• pISSN

  2233-7849

• 수록기간

  2009~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605