A Fusion of the Period Characterized and Hierarchical Bayesian Techniques for Efficient Cluster Analysis of Time Series Data
Young-Ae Jung, Jin-Ho Jeon · Journal of Digital Convergence · 2015
Abstract 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. Key Words : Period Characterized, Bayesian, Time Series Data, Fusion, Clustering Received 16 April 2015, Revised 20 May 2015Accepted 20 July 2015Corresponding Author: Jin-Ho Jeon(Catholic Kwan-Dong University) Email: [email protected]Ⓒ The Society of Digital Policy & Management. All rights reserved. This is an open-access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is ISSN: 1738-1916 properly cited.