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Coupled Behavior Analysis with Applications
Aug. 2012 (vol. 24 no. 8)
pp. 1378-1392
ASCII Text
x 
 
Longbing Cao, Yuming Ou, Philip S. Yu, "Coupled Behavior Analysis with Applications," IEEE Transactions on Knowledge and Data Engineering, vol. 24, no. 8, pp. 1378-1392, Aug., 2012.
 
 
BibTex
x
 
@article{ 10.1109/TKDE.2011.129,
author = {Longbing Cao and Yuming Ou and Philip S. Yu},
title = {Coupled Behavior Analysis with Applications},
journal ={IEEE Transactions on Knowledge and Data Engineering},
volume = {24},
number = {8},
issn = {1041-4347},
year = {2012},
pages = {1378-1392},
doi = {http://doi.ieeecomputersociety.org/10.1109/TKDE.2011.129},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Knowledge and Data Engineering
TI - Coupled Behavior Analysis with Applications
IS - 8
SN - 1041-4347
SP1378
EP1392
EPD - 1378-1392
A1 - Longbing Cao,
A1 - Yuming Ou,
A1 - Philip S. Yu,
PY - 2012
KW - Coupled behavior analysis
KW - coupled sequence analysis
KW - hidden group discovery
KW - coupled hidden Markov model
KW - abnormal behavior detection.
VL - 24
JA - IEEE Transactions on Knowledge and Data Engineering
ER -
 
Longbing Cao, University of Technology, Sydney
Yuming Ou, University of Technology, Sydney
Philip S. Yu, University of Illinois at Chicago, Chicago
Coupled behaviors refer to the activities of one to many actors who are associated with each other in terms of certain relationships. With increasing network and community-based events and applications, such as group-based crime and social network interactions, behavior coupling contributes to the causes of eventual business problems. Effective approaches for analyzing coupled behaviors are not available, since existing methods mainly focus on individual behavior analysis. This paper discusses the problem of Coupled Behavior Analysis (CBA) and its challenges. A Coupled Hidden Markov Model (CHMM)-based approach is illustrated to model and detect abnormal group-based trading behaviors. The CHMM models cater for: 1) multiple behaviors from a group of people, 2) behavioral properties, 3) interactions among behaviors, customers, and behavioral properties, and 4) significant changes between coupled behaviors. We demonstrate and evaluate the models on order-book-level stock tick data from a major Asian exchange and demonstrate that the proposed CHMMs outperforms HMM-only for modeling a single sequence or combining multiple single sequences, without considering coupling relationships to detect anomalies. Finally, we discuss interaction relationships and modes between coupled behaviors, which are worthy of substantial study.

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Index Terms:
Coupled behavior analysis, coupled sequence analysis, hidden group discovery, coupled hidden Markov model, abnormal behavior detection.
Citation:
Longbing Cao, Yuming Ou, Philip S. Yu, "Coupled Behavior Analysis with Applications," IEEE Transactions on Knowledge and Data Engineering, vol. 24, no. 8, pp. 1378-1392, Aug. 2012, doi:10.1109/TKDE.2011.129
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