loading...
 This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
Fourth IEEE International Conference on Data Mining (ICDM'04)
Extensible Markov Model
Brighton, United Kingdom
November 01-November 04
ISBN: 0-7695-2142-8
ASCII Text
x 
 
Margaret H. Dunham, Yu Meng, Jie Huang, "Extensible Markov Model," Data Mining, IEEE International Conference on, pp. 371-374, Fourth IEEE International Conference on Data Mining (ICDM'04), 2004.
 
 
BibTex
x
 
@article{ 10.1109/ICDM.2004.10067,
author = {Margaret H. Dunham and Yu Meng and Jie Huang},
title = {Extensible Markov Model},
journal ={Data Mining, IEEE International Conference on},
volume = {0},
year = {2004},
isbn = {0-7695-2142-8},
pages = {371-374},
doi = {http://doi.ieeecomputersociety.org/10.1109/ICDM.2004.10067},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - Data Mining, IEEE International Conference on
TI - Extensible Markov Model
SN - 0-7695-2142-8
SP371
EP374
A1 - Margaret H. Dunham,
A1 - Yu Meng,
A1 - Jie Huang,
PY - 2004
KW - null
VL - 0
JA - Data Mining, IEEE International Conference on
ER -
 
Margaret H. Dunham, Southern Methodist University, Dallas, Texas
Yu Meng, Southern Methodist University, Dallas, Texas
Jie Huang, The University of Texas Southwestern, Dallas, Texas
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.
Citation:
Margaret H. Dunham, Yu Meng, Jie Huang, "Extensible Markov Model," icdm, pp.371-374, Fourth IEEE International Conference on Data Mining (ICDM'04), 2004
Usage of this product signifies your acceptance of the Terms of Use.