Journal of the Korea Convergence Society (한국융합학회논문지)

Korea Convergence Society (한국융합학회)
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Knowledge Reasoning Model using Association Rules and Clustering Analysis of Multi-Context

다중상황의 군집분석과 연관규칙을 이용한 지식추론 모델

  • Shin, Dong-Hoon (Department of Computer Science, Kyonggi University) ;
  • Kim, Min-Jeong (Department of Computer Science, Kyonggi University) ;
  • Oh, SangYeob (Division of Computer Engineering, Gachon University) ;
  • Chung, Kyungyong (Division of Computer Science and Engineering, Kyonggi University)
  • 신동훈 (경기대학교 컴퓨터과학과) ;
  • 김민정 (경기대학교 컴퓨터과학과) ;
  • 오상엽 (가천대학교 컴퓨터공학과) ;
  • 정경용 (경기대학교 컴퓨터공학부)
  • Received : 2019.07.25
  • Accepted : 2019.09.20
  • Published : 2019.09.28
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Abstract

People are subject to time sanctions in a busy modern society. Therefore, people find it difficult to eat simple junk food and even exercise, which is bad for their health. As a result, the incidence of chronic diseases is increasing. Also, the importance of making accurate and appropriate inferences to individual characteristics is growing due to unnecessary information overload phenomenon. In this paper, we propose a knowledge reasoning model using association rules and cluster analysis of multi-contexts. The proposed method provides a personalized healthcare to users by generating association rules based on the clusters based on multi-context information. This can reduce the incidence of each disease by inferring the risk for each disease. In addition, the model proposed by the performance assessment shows that the F-measure value is 0.027 higher than the comparison model, and is highly regarded than the comparison model.

사람들은 바쁜 현대사회 속에서 시간적 제재를 받고 있다. 이에 따라 사람들은 건강에 나쁜 영향을 미치는 간편한 인스턴트 식품을 섭취하고 간단한 운동조차하기 어려운 상황에 놓여있다. 또한 불필요한 정보과부화 현상으로 인해 개인의 특성에 적합하고 정확한 추론을 하는 것에 대한 중요성이 커지고 있다. 따라서 본 논문에서는 다중상황의 군집분석과 연관규칙을 이용한 지식추론 모델을 제안한다. 제안하는 방법은 상황정보에 따른 군집을 기반으로 연관규칙을 생성함으로써 사용자들에게 개인화된 헬스케어 방법을 제공한다. 이를 통해 각 질병에 대한 위험도를 추론함으로써 해당 질병에 대한 발병률을 낮출 수 있다. 또한 성능 평가를 통해 제안하는 모델이 비교 모델보다 수치상으로 F-measure 값이 0.027 더 높게 나타나며, 비교 모델 보다 우수하게 평가된다.

Keywords

References

  1. K. Chung & R. C. Park (2019). Cloud based U-healthcare Network with QoS Guarantee for Mobile Health Service, Cluster Computing, 22(1), 2001-2015. https://doi.org/10.1007/s10586-017-1120-0
  2. R. Zhao, W. Ouyang, H. Li & X. Wang. (2015). Saliency Detection by Multi-Context Deep Learning. In Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, 1265-1274.
  3. Korea Centers for Disease Control and Prevention (KCDC). http://www.cdc.go.kr/.
  4. G. O. Kim, G. S. Lee & S. H. Lee. (2014). An Edge Extraction Method using K-means Clustering in Image. Journal of Digital Convergence, 12(11), 281-288. https://doi.org/10.14400/JDC.2014.12.11.281
  5. P. Langfelder, B. Zhang & S. Horvath. (2007). Defining Clusters from a Hierarchical Cluster Tree: The Dynamic Tree Cut Package for R. Bioinformatics, 24(5), 719-720. https://doi.org/10.1093/bioinformatics/btm563
  6. J. A. Hartigan & M. A. Wong. (1979). Algorithm AS 136: A K-means Clustering Algorithm. Journal of the Royal Statistical Society. Series C, 28(1), 100-108.
  7. T. Hastie, R. Tibshirani, J. Friedman, J. Franklin. (2005). The Elements of Statistical Learning: Data Mining, Inference and Prediction. The Mathematical Intelligencer, 27(2), 83-85.
  8. J. C. Kim & K. Chung. (2019). Associative Feature Information Extraction using Text Mining from Health Big Data. Wireless Personal Communications, 105(2), 691-707. https://doi.org/10.1007/s11277-018-5722-5
  9. K. T. Oh & S. Y. Lee. (2019). A Movie Recommendation System processing High-Dimensional Data with Fuzzy-AHP and Fuzzy Association Rules. Journal of Digital Convergence, 17(2), 347-353. https://doi.org/10.14400/JDC.2019.17.2.347
  10. S. Zhang. (2011). An Apriori-based Algorithm of Association Rules based on Cloud Computing. Communications Technology, 44(6), 141-143. https://doi.org/10.3969/j.issn.1002-0802.2011.06.049
  11. C. Borgelt. (2005). An Implementation of the FP-growth Algorithm, In Proc. of the International Workshop on Open Source Data Mining: Frequent Pattern Mining Implementations, 1-5.
  12. J. C. Kim & K. Chung. (2017). Emerging Risk Forecast System using Associative Index Mining Analysis. Cluster Computing, 20(1), 547-558. https://doi.org/10.1007/s10586-016-0702-6
  13. D. M. Powers. (2011). Evaluation: from Frecision, Recall and F-measure to ROC, Informedness, Markedness and Correlation. Journal of Machine Learning Technologies, 2(1), 37-63.
  14. J. Davis & M. Goadrich. (2006). The Relationship between Precision-Recall and ROC Curves. In Proc. of the International Conference on Machine Learning, 233-240.
  15. P. S. Bradley & U. M. Fayyad. (1998). Refining Initial Points for K-Means Clustering. In Proc. of the International Conference on Machine Learning, 91-99.