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Novel Bayesian Cluster Enumeration Criterion For Cluster Analysis With Finite Sample Penalty Term

Abstract: 

The Bayesian information criterion is generic in the sense that it does not include information about the specific model selection problem at hand. Nevertheless, it has been widely used to estimate the number of data clusters in cluster analysis. We have recently derived a Bayesian cluster enumeration criterion from first principles which maximizes the posterior probability of the candidate models given observations. But, in the finite sample regime, the asymptotic assumptions made by the criterion, to arrive at a computationally simple penalty term, are violated. Hence, we propose a Bayesian cluster enumeration criterion whose penalty term is derived by removing the asymptotic assumptions. The proposed algorithm is a twostep
approach which uses a model-based clustering algorithm such as the EM algorithm before applying the derived criterion.
Simulation results demonstrate the superiority of our criterion over existing Bayesian cluster enumeration criteria.

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Paper Details

Authors:
Frewweyni K. Teklehaymanot, Michael Muma, Abdelhak M. Zoubir
Submitted On:
14 April 2018 - 7:52am
Short Link:
Type:
Poster
Event:
Presenter's Name:
Freweyni K. Teklehaymanot
Paper Code:
4422
Document Year:
2018
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Bayesian Cluster Enumeration Criterion

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[1] Frewweyni K. Teklehaymanot, Michael Muma, Abdelhak M. Zoubir, "Novel Bayesian Cluster Enumeration Criterion For Cluster Analysis With Finite Sample Penalty Term", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2820. Accessed: Sep. 20, 2018.
@article{2820-18,
url = {http://sigport.org/2820},
author = {Frewweyni K. Teklehaymanot; Michael Muma; Abdelhak M. Zoubir },
publisher = {IEEE SigPort},
title = {Novel Bayesian Cluster Enumeration Criterion For Cluster Analysis With Finite Sample Penalty Term},
year = {2018} }
TY - EJOUR
T1 - Novel Bayesian Cluster Enumeration Criterion For Cluster Analysis With Finite Sample Penalty Term
AU - Frewweyni K. Teklehaymanot; Michael Muma; Abdelhak M. Zoubir
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2820
ER -
Frewweyni K. Teklehaymanot, Michael Muma, Abdelhak M. Zoubir. (2018). Novel Bayesian Cluster Enumeration Criterion For Cluster Analysis With Finite Sample Penalty Term. IEEE SigPort. http://sigport.org/2820
Frewweyni K. Teklehaymanot, Michael Muma, Abdelhak M. Zoubir, 2018. Novel Bayesian Cluster Enumeration Criterion For Cluster Analysis With Finite Sample Penalty Term. Available at: http://sigport.org/2820.
Frewweyni K. Teklehaymanot, Michael Muma, Abdelhak M. Zoubir. (2018). "Novel Bayesian Cluster Enumeration Criterion For Cluster Analysis With Finite Sample Penalty Term." Web.
1. Frewweyni K. Teklehaymanot, Michael Muma, Abdelhak M. Zoubir. Novel Bayesian Cluster Enumeration Criterion For Cluster Analysis With Finite Sample Penalty Term [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2820