The KIPS Transactions:PartD (정보처리학회논문지D)

Korea Information Processing Society (한국정보처리학회)
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XOnto-Apriori: An eXtended Ontology Reasoning-based Association Rule Mining Algorithm

XOnto-Apriori: 확장된 온톨로지 추론 기반의 연관 규칙 마이닝 알고리즘

  • 이종현 (고려대학교 컴퓨터.전파통신공학과) ;
  • 김장원 (고려대학교 컴퓨터.전파통신공학과) ;
  • 정동원 (군산대학교 정보통계학과) ;
  • 이석훈 (고려대학교 컴퓨터.전파통신공학과) ;
  • 백두권 (고려대학교 컴퓨터.전파통신공학과)
  • Received : 2011.08.23
  • Accepted : 2011.10.27
  • Published : 2011.12.31
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Abstract

In this paper, we introduce XOnto-Apriori algorithm which is an extension of the Onto-Apriori algorithm. The extended algorithm is designed to improve the conventional algorithm's problem of comparing only identifiers of transaction items by reasoning transaction properties of the items which belong in the same category. We show how the mining algorithm works with a smartphone application recommender system based on our extended algorithm to clearly describe the procedures providing personalized recommendations. Further, our simulation results validate our analysis on the algorithm overhead, precision, and recall.

이 논문에서는 연관 규칙 마이닝 알고리즘의 정확도를 향상시키기 위하여 기존 Onto-Apriori 알고리즘을 확장한 XOnto-Apriori 알고리즘을 제안한다. 기존 알고리즘은 트랜잭션 항목의 식별자만을 비교하여 지지도를 계산하기 때문에 유사한 속성을 가진 항목들간의 관계를 분석하지 못하는 문제점을 지닌다. 이러한 문제점을 해결하기 위해 제안 알고리즘은 온톨로지 추론 기반의 속성 비교를 통해 같은 식별자를 지니지 않는 항목들간의 관계성도 지지도 계산에 반영할 수 있도록 한다. 제안 알고리즘의 규칙 생성 과정을 명확히 서술하기 위해 스마트폰 어플리케이션 추천 시스템을 설계하였으며 이 시스템은 기존 알고리즘 기반의 시스템에 비해 보다 나은 속도와 정확도를 보였다.

Keywords

References

  1. A. Felferning, M. Mandl, J. Tihonen, M. Schubert, and G. Leitner, "Personalized user interfaces for product configuration", ACM Proceeding of the 14th international conference on Intelligent user interfaces, pp.317-320, 2010. https://doi.org/10.1145/1719970.1720020
  2. Y. Wu, Y. Chen, and A.L.P. Chen, "Enabling personalized recommendation on the Web based on user interests and behaviors", Research Issues in Data Engineering Proceedings. Eleventh International Workshop on, pp.17-24, 2001. https://doi.org/10.1109/RIDE.2001.916487
  3. S.Y. Ho and S.H. Kwok, "The Attraction of Personalized Service for Users in Mobile Commerce: An Empirical Study", ACM SIGecom Exchanges, Vol.3, No.4, pp.10-18, 2003.
  4. G. Linden, B. Smith, and J. York, "Amazon.com recommendations: item-to-item collaborative filtering", Internet Computing, IEEE Computer Society, pp.76-80, 2003. https://doi.org/10.1109/MIC.2003.1167344
  5. J. Choi, H. Lee, and Y. Kim, "The Influence of Social Presence on Evaluating Personalized Recommender Systems", Pacific Asia Conference on Information Systems(PACIS), AIS Electronic Library (AISeL), 2009.
  6. V. Tamma, "Semantic Web Support for Intelligent Search and Retrieval of Business Knowledge", IEEE Intelligent Systems, Vol.25, No.1, pp.84-88, 2010. https://doi.org/10.1109/MIS.2010.25
  7. C. Lee, S. Lee, J. Kim, D. Baik, "A Personalized Clothing Recommender System Based on the Algorithm for Mining Association Rules", Journal of Korea Society for Simulation, Vol.19, No.4, pp.59-66, 2010
  8. X. Y. Su and Taghi M. Khoshgoftaar, "A survey of collaborative lerting techniques. Advances in Articial Intelligence", Vol.2009, No.4, 2009.
  9. M. J. Pazzani, "A Framework for Collaborative, Content-Based and Demographic Filtering", Artificial Intelligence Review, Vol.13, No.5-6, pp.393-408, 1999. https://doi.org/10.1023/A:1006544522159
  10. P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom, and J. Riedl, "GroupLens: An Open Architecture for Collaborative Filtering of Netnews", In Proc. of ACM CSCW'94 Conference on Computer Supported Cooperative Work, pp.175-186, 1994.
  11. J. Herlocker, J. Konstan, A. Borchers, and J. Riedl, "An Algorithm Framework for Performing Collaborative Filtering", In Proc. of ACM SIGIR'99, 1999.
  12. J. S. Breese, D. Heckerman, and C. Kadie, "Empirical Analysis of Predictive Algorithms for Collaborative Filtering", In Proc. of the 14th Conference on Uncertainty in Artificial Intelligence, 1998. https://doi.org/10.1111/j.1553-2712.2011.01172.x
  13. R. Missaoui, P. Valtchev, C. Djeraba, and M. Adda, "Toward Recommendation Based on Ontology-Powered Web-Usage Mining", IEEE Internet Computing, Vol.11, No.4, pp.45-52, 2007. https://doi.org/10.1109/MIC.2007.93
  14. J. Davis and M. Goadrich, "The relationship between precision-recall and roc curves", In ICML S06: Proceedings of the 23rd international conference on Machine learning, pp.233-240, 2006.
  15. T. Y. Chen, F. Kuo, and R. Merkel, "On the Statistical Properties of the F-Measure", Quality Software Fourth International Conference on (QSIC'04), pp.146-153, 2004. https://doi.org/10.1109/QSIC.2004.1357955