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Growing Self Organizing Map with an Imposed Binary Search Tree for Discovering Temporal Input Patterns

Matharage, Sumith, Gunasinghe, Upuli and Alahakoon, Damminda 2009, Growing Self Organizing Map with an Imposed Binary Search Tree for Discovering Temporal Input Patterns, in Proceedings of the Fourth International Conference on Industrial and Information Systems (ICIIS); IEEE 2009, IEEE, Washington, DC, pp. 222-226, doi: 10.1109/ICIINFS.2009.5429862.

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Title Growing Self Organizing Map with an Imposed Binary Search Tree for Discovering Temporal Input Patterns
Author(s) Matharage, Sumith
Gunasinghe, Upuli
Alahakoon, Damminda
Conference name International Conference on Industrial and Information Systems (4th : 2009 : Peradeniya, Sri Lanka)
Conference location Peradeniya, Sri Lanka
Conference dates 28-31 Dec. 2009
Title of proceedings Proceedings of the Fourth International Conference on Industrial and Information Systems (ICIIS); IEEE 2009
Editor(s) Ekanayaka, E MN
Publication date 2009
Conference series International Conference on Industrial and Information Systems
Start page 222
End page 226
Total pages 5
Publisher IEEE
Place of publication Washington, DC
Keyword(s) Growing Self Organising Maps
Tree based SOM
Summary In this paper the Binary Search Tree Imposed Growing Self Organizing Map (BSTGSOM) is presented as an extended version of the Growing Self Organizing Map (GSOM), which has proven advantages in knowledge discovery applications. A Binary Search Tree imposed on the GSOM is mainly used to investigate the dynamic perspectives of the GSOM based on the inputs and these generated temporal patterns are stored to further analyze the behavior of the GSOM based on the input sequence. Also, the performance advantages are discussed and compared with that of the original GSOM.
Notes This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.
ISBN 1424448379
9781424448371
Language eng
DOI 10.1109/ICIINFS.2009.5429862
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1.1 Full written paper - refereed
Copyright notice ©2009, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30060462

Document type: Conference Paper
Collections: School of Information and Business Analytics
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Created: Fri, 14 Feb 2014, 12:28:51 EST by Sumith Matharage

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.