HDGSOM: A Modified Growing Self-Organizing Map for High Dimensional Data Clustering

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Fourth International Conference on Hybrid Intelligent Systems (HIS'04)

Kitakyushu, Japan

December 05-December 08

ISBN: 0-7695-2291-2

	ASCII Text	x
	Rasika Amarasiri, Damminda Alahakoon, Kate A. Smith, "HDGSOM: A Modified Growing Self-Organizing Map for High Dimensional Data Clustering," Hybrid Intelligent Systems, International Conference on, pp. 216-221, Fourth International Conference on Hybrid Intelligent Systems (HIS'04), 2004.

	BibTex	x
	@article{ 10.1109/ICHIS.2004.52, author = {Rasika Amarasiri and Damminda Alahakoon and Kate A. Smith}, title = {HDGSOM: A Modified Growing Self-Organizing Map for High Dimensional Data Clustering}, journal ={Hybrid Intelligent Systems, International Conference on}, volume = {0}, year = {2004}, isbn = {0-7695-2291-2}, pages = {216-221}, doi = {http://doi.ieeecomputersociety.org/10.1109/ICHIS.2004.52}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - CONF JO - Hybrid Intelligent Systems, International Conference on TI - HDGSOM: A Modified Growing Self-Organizing Map for High Dimensional Data Clustering SN - 0-7695-2291-2 SP216 EP221 A1 - Rasika Amarasiri, A1 - Damminda Alahakoon, A1 - Kate A. Smith, PY - 2004 KW - null VL - 0 JA - Hybrid Intelligent Systems, International Conference on ER -

Rasika Amarasiri, Monash University, Australia

Damminda Alahakoon, Monash University, Australia

Kate A. Smith, Monash University, Australia

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICHIS.2004.52

The Growing Self Organizing Map (GSOM) algorithm is a variant of the Self Organizing Map (SOM). It has a dynamically growing structure that adapts to the natural structure of the data. It has been identified that the growing of the GSOM can get negatively affected when used with very large dimensional data such as those in text and DNA data sets. This paper addresses these issues and presents a modified version of the GSOM called the High Dimensional GSOM (HDGSOM). The algorithm and experimental results showing the improved performance of the HDGSOM are also presented.

Citation:

Rasika Amarasiri, Damminda Alahakoon, Kate A. Smith, "HDGSOM: A Modified Growing Self-Organizing Map for High Dimensional Data Clustering," his, pp.216-221, Fourth International Conference on Hybrid Intelligent Systems (HIS'04), 2004

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