DOI QR코드

DOI QR Code

범주형 데이터의 러프집합 분석을 통한 의사결정 향상기법

An Improvement of the Decision-Making of Categorical Data in Rough Set Analysis

  • 박인규 (중부대학교 컴퓨터.게임공학과)
  • 투고 : 2015.03.20
  • 심사 : 2015.06.20
  • 발행 : 2015.06.28

초록

최적의 의사결정시스템에서 효율적인 정보검색을 위해서는 정보의 감축이 필수적이다. 다양한 종류의 데이터에 존재하는 유용한 정보를 찾는 데이터 마이닝 기법에 대한 많은 연구가 활발하게 진행되어 왔고 타 산업과의 융복합을 위한 빅데이터 활용이 높아져 가고 있다. 유용한 지식의 발견을 위한 여러 가지 기법들이 특징을 가지고 있지만 단점이 존재하기 마련이다. 따라서 그러한 특징을 수렴하는 하나의 새로운 기법이 필요하다. 본 논문에서는 베이지언 정리를 이용하여 정보의 대수학적인 확률을 측정하고 이 확률에 대하여 정보엔트로피를 계산함으로써 정보의 불확실성을 계산한다. 제안된 척도를 기반으로 불필요한 속성을 제거하여 최소의 리덕트를 생성하고 이를 기반으로 결정규칙을 유도하는 알고리즘을 제안한다. 제안된 방법의 효율성을 위하여 콘텍트 렌즈를 결정하는 실험을 통하여 기존방법과 비교 결과, 본 연구가 의사결정의 유용성면에 있어 일반성이 있음을 보인다.

An efficient retrieval of useful information is a prerequisite of an optimal decision making system. Hence, A research of data mining techniques finding useful patterns from the various forms of data has been progressed with the increase of the application of Big Data for convergence and integration with other industries. Each technique is more likely to have its drawback so that the generalization of retrieving useful information is weak. Another integrated technique is essential for retrieving useful information. In this paper, a uncertainty measure of information is calculated such that algebraic probability is measured by Bayesian theory and then information entropy of the probability is measured. The proposed measure generates the effective reduct set (i.e., reduced set of necessary attributes) and formulating the core of the attribute set. Hence, the optimal decision rules are induced. Through simulation deciding contact lenses, the proposed approach is compared with the equivalence and value-reduct theories. As the result, the proposed is more general than the previous theories in useful decision-making.

키워드

참고문헌

  1. E.A. Kweon, H.G. Kim, "Simplication of control rules using probabilistic rough set", Journal of Information Processing, Vol. 8-D, No. 3, pp.203-210, 2001.
  2. E. Satake, A. V. Murray, "Teaching an Application of Bayes' Rule for Legal Decision-Making: Measuring the Strength of Evidence", Journal of Statistics Education, Vol. 22, No.1, 2014.
  3. H.S. Kim, H.G. Kim, S.B. Lee, "Retrieval of fuzzy information based on probabilistic rough sets", Journal of Information Science, Vol. 25, No. 9, pp. 1431-1441, 2005.
  4. G. J. Williams and S. J. Simoff, "Data mining theory, methodology, Techniques and Applications (Lecture Notes in Computer Science/Lecture Notes in Artificial Intelligence)", Springer, 2007.
  5. In-Kyoo Park. "The generation of control rules for data mining", The Journal of Digital Policy & Management, Vol. 11, No.1, pp.343-349, 2013.
  6. R. Vashist, M.L. Garg, "Rule generation based on reduct and core: a rough set approach", International Journal of Computer Applications, Vol. 29, No. 9, pp. 0975-8887, Sept. 2011.
  7. S.K. Pal and A. Skowron, "Rough Fuzzy Hybridization: A new trend in decision making", Springer Verlag, Berlin, 1999.
  8. T. Beaubouef, F. E. Petry and G. Arora, "Information-theoretic measures of uncertainty for rough sets and rough relational databases", Information Science, Vol. 109, No. 1-4, pp. 185-195, 1998. https://doi.org/10.1016/S0020-0255(98)00019-X
  9. Y. C. Tsai, C. H. Cheng and J. R. Chang, "Entropy-based fuzzy rough classification approach for extracting classification rules", Expert Systems with Applications Vol. 31, pp. 436-443, 2006. https://doi.org/10.1016/j.eswa.2005.09.038
  10. Z. Pawlak, "Rough sets", International Journal of Information Sciences, Vol.11, No. 5, pp. 341-356, 1982. https://doi.org/10.1007/BF01001956
  11. Z. Pawlak, "Rough sets: Theoretical aspects of reasoning about data", Kluwer Academic Publishers, 1991.
  12. J. Liang, Z. Shi, D. Li, M.J. Wierman, "Information entropy, rough entropy and knowledge granulation in incomplete information systems", International Journal of Genreal Systems, Vol. 35, No. 6, pp. 641-654, December, 2006. https://doi.org/10.1080/03081070600687668
  13. Q. Shen, R. Jensen, "Rough sets, their Extensions and Applications", International Journal of Automation Computing, Vol. 4, No. 1, pp. 100-106, 2007.
  14. Chua Hong Siang, Sanghyuk Lee, "Information Management by Data Quantification with FuzzyEntropy and Similarity Measure", Journal of the Korea Convergence Society, Vol. 4, No. 2, pp. 35-41, 2013. https://doi.org/10.15207/JKCS.2013.4.2.035
  15. Sang-Hyun Lee, "A Study on Determining Factors for Manufacturers to Distributors Warehouse in Supply Chain", Journal of the Korea Convergence Society, Vol. 4, No. 2, pp. 15-20, 2013. https://doi.org/10.15207/JKCS.2013.4.2.015