A Fusion of the Period Characterized and Hierarchical Bayesian Techniques for Efficient Cluster Analysis of Time Series Data

시계열자료의 효율적 군집분석을 위한 구간특징화와 계층적 베이지안 기법의 융합

  • Jung, Young-Ae (Dept. of Information Technology Education, Sun-Moon University) ;
  • Jeon, Jin-Ho (Dept. of Business Administration, Catholic Kwan-Dong University)
  • 정영애 (선문대학교 IT학부) ;
  • 전진호 (가톨릭관동대학교 경영학과)
  • Received : 2015.04.16
  • Accepted : 2015.07.20
  • Published : 2015.07.28


An effective way to understand the dynamic and time series that follows the passage of time, as valuation is to establish a model to analyze the phenomena of the system. Model of the decision process is efficient clustering information of the total mass of the time series data of the relevant population been collected in a particular number of sub-groups than to look at all a time to an understand of the overall data through each community-specific model determination. In this study, a sub-grouping of the group and the first of the two process model of each cluster by determining, in the following in sub-population characterized by a fusion with heuristic Bayesian clustering techniques proposed a process which can reduce calculation time and cost was confirmed by experiments using actual effectiveness valuation.


Period Characterized;Bayesian;Time Series Data;Fusion;Clustering


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