Experimental Evaluation of Distance-based and Probability-based Clustering

  • Kwon, Na Yeon ;
  • Kim, Jang Il ;
  • Dollein, Richard ;
  • Seo, Weon Joon ;
  • Jung, Yong Gyu
  • Received : 2013.03.26
  • Published : 2013.05.31


Decision-making is to extract information that can be executed in the future, it refers to the process of discovering a new data model that is induced in the data. In other words, it is to find out the information to peel off to find the vein to catch the relationship between the hidden patterns in data. The information found here, is a process of finding the relationship between the useful patterns by applying modeling techniques and sophisticated statistical analysis of the data. It is called data mining which is a key technology for marketing database. Therefore, research for cluster analysis of the current is performed actively, which is capable of extracting information on the basis of the large data set without a clear criterion. The EM and K-means methods are used a lot in particular, how the result values of evaluating are come out in experiments, which are depending on the size of the data by the type of distance-based and probability-based data analysis.


EM;Decision-making;K-Means;Maximization Step;BI-RADS assessment


  1. Stuart Moran, Yulan Hey, Kecheng Liu, "An Empirical Framework for Automatically Selecting the Best Bayesian Classifier", Proceedings of the World Congress on Engineering 2009 Vol I, WCE 2009, July 1 - 3, 2009.
  2. Carolina Ruiz, "Illustration of the K2 Algorithm for Learning Bayes Net Structures", Department of Computer Science, WPI, 2005.
  3. EvelinaLamma, FabrizioRiguzzi, Sergio Storari, "Improving the K2 Algorithm Using Association Rule Parameters", Modern Information Processing: From Theory to Applications B, 2006.
  4. Jesse Davis and Pedro Domingos (2010). Bottom-Up Learning of Markov Network Structure. In the Proceedings of the 27th International Conference on Machine Learning (ICML).
  5. Yong Gyu Jung, Go Eun Heo, Ensemble Classification Method for Efficient Medical Diagnostic, Journal of the Institute of Webcasting, Internet Television and Telecommunication (IWIT), Vol.10. No.3 p97-p102, June 2010
  6. JesusCerquides, "Tractable Bayesian Learning of Tree Augmented Naive Bayes Classifiers", Ramon Lopez de Mantaras, 2003.
  7. Yong Gyu Jung, Jong Han Lim, Automobile Traffic Accidents Prediction Model using by Artificial Neural Networks, ICHIT2012, Communications in Computer and Information Science, Vol.310,p713-p719
  8. Yong Gyu Jung, Song Ei Han Ranking Methods of Web Search using Genetic Algorithm, Journal of the Institute of Webcasting, Internet Television and Telecommunication (IWIT), Vol.10 No.3 p91-p96, June 2010