Clustering Analysis of Effective Health Spending Cost based on Kernel Filtering Techniques

커널필터링 기법을 이용한 건강비용의 효과적인 지출에 관한 군집화 분석

  • Received : 2015.08.11
  • Accepted : 2015.09.23
  • Published : 2015.09.30


As Data mining is a method of extracting the information based on the large data, the technique has been used in many application areas to deal with data in particular. However, the status of the algorithm that can deal with the healthcare data are not fully developed. In this paper, One of clustering algorithm, the EM and DBSCAN are used for performance comparison. It could be analyzed using by the same data. To do this, EM and DBSACN algorithm are changing performance according to the variables in Health expenditure database. Based on the results of the experimental data, We analyze more precise and accurate results using by Kernel Filtering. In this study, we tried comparison of the performance for the algorithm as well as attempt to improve the performance. Through this work, we were analyzed the comparison result of the application of the experimental data and of performance change according to expansion algorithm. Especially, Collects data from the various cluster using the medical record, it could be recommended the effective spending on medical services.


Health Expenditure Data;EM;DBSCAN;Clustering;Regression Analysis


  1. Doddi, S., Achla Marathe, Ravi, S. S., and Torney, D. C. (2001), "Discovery of association rules in medical data", Informatics for Health and Social Care, 26(1), 25-33.
  2. Hosking, J. R. M. and Wallis, J. R. (2005), Regional frequency analysis: an approach based on Lmoments, Cambridge University Press.
  3. Kirchhoff, W. H. (2012), "LOGISTIC FUNCTION PROFILE FIT: A least-squares program for fitting interface profiles to an extended logistic functiona)", Journal of Vacuum Science and Technology, A 30.5, 051101.
  4. Malefaki, S., Trevezas, S., and Limnios, N. (2010), "An EM and a stochastic version of the EM algorithm for nonparametric Hidden semi-Markov models", Communications in Statistics-Simulation and Computation(R), 39(2), 240-261.
  5. Palaniappan, S. and Awang, R. (2008), "Intelligent heart disease prediction system using data mining techniques", 108-115.
  6. Witten, I. H. and Frank, E. (2005), Data Mining: Practical machine learning tools and techniques, Morgan Kaufmann.