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. Attached Files Name Description MIMEType Size Downloads matharage-growingself-2009.pdf Published version application/pdf 807.69KB 223 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 Free to Read? Yes Persistent URL http://hdl.handle.net/10536/DRO/DU:30060462 Document type: Conference Paper Collections: School of Information and Business Analytics Open Access Collection Related Links Link Description Connect to pucblished version (restricted access) Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. 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. Versions Version Filter Type Fri, 14 Feb 2014, 12:29:09 EST Mon, 17 Feb 2014, 08:25:03 EST Tue, 18 Feb 2014, 09:58:28 EST Tue, 18 Feb 2014, 14:37:22 EST Tue, 18 Feb 2014, 14:38:33 EST Mon, 17 Mar 2014, 13:31:57 EST Mon, 14 Jul 2014, 15:33:17 EST Mon, 15 Sep 2014, 15:06:34 EST Fri, 10 Oct 2014, 09:15:04 EST Tue, 06 Sep 2016, 22:54:02 EST Wed, 06 Dec 2017, 06:02:32 EST Fri, 07 Sep 2018, 16:55:39 EST Filtered Full Citation counts:   Cited 1 times in TR Web of Science Cited 2 times in Scopus Search Google Scholar Access Statistics: 168 Abstract Views, 223 File Downloads  -  Detailed Statistics Created: Fri, 14 Feb 2014, 12:28:51 EST by Sumith Matharage