The Community for Technology Leaders
Fourth IEEE International Conference on Data Mining (ICDM'04) (2004)
Brighton, United Kingdom
Nov. 1, 2004 to Nov. 4, 2004
ISBN: 0-7695-2142-8
pp: 371-374
Margaret H. Dunham , Southern Methodist University, Dallas, Texas
Yu Meng , Southern Methodist University, Dallas, Texas
Jie Huang , The University of Texas Southwestern, Dallas, Texas
ABSTRACT
A Markov Chain is a popular data modeling tool. This paper presents a variation of Markov Chain, namely Extensible Markov Model (EMM). By providing a dynamically adjustable structure, EMM overcomes the problems caused by the static nature of the traditional Markov Chain. Therefore, EMMs are particularly well suited to model spatiotemporal data such as network traffic, environmental data, weather data, and automobile traffic. Performance studies using EMMs for spatiotemporal prediction problems show the advantages of this approach.
INDEX TERMS
null
CITATION

Y. Meng, J. Huang and M. H. Dunham, "Extensible Markov Model," Fourth IEEE International Conference on Data Mining (ICDM'04)(ICDM), Brighton, United Kingdom, 2004, pp. 371-374.
doi:10.1109/ICDM.2004.10067
97 ms
(Ver 3.3 (11022016))