Extensible Markov Model

Authors:
Margaret H. Dunham;Yu Meng;Jie Huang
Affiliations:
Southern Methodist University, Dallas, Texas;Southern Methodist University, Dallas, Texas;The University of Texas Southwestern, Dallas, Texas
Venue:
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Year:
2004

Citing 0
Cited 3

A Comparative Study of Outlier Detection Algorithms

MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
Efficient mining of emerging events in a dynamic spatiotemporal environment

PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Data stream clustering: A survey

ACM Computing Surveys (CSUR)

Quantified Score

Hi-index	0.00

Visualization

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.