Clustering massive high dimensional data with dynamic feature maps Amarasiri, Rasika, Alahakoon, Damminda and Smith-Miles, Kate 2006, Clustering massive high dimensional data with dynamic feature maps, Lecture notes in computer science, vol. 4233, pp. 814-823, doi: 10.1007/11893257_90. Attached Files Name Description MIMEType Size Downloads Title Clustering massive high dimensional data with dynamic feature maps Author(s) Amarasiri, Rasika Alahakoon, Damminda Smith-Miles, Kate Journal name Lecture notes in computer science Volume number 4233 Start page 814 End page 823 Total pages 9 Publisher Springer-Verlag Place of publication Berlin, Germany Publication date 2006 ISSN 0302-9743 1611-3349 Keyword(s) growing self organizing map GSOM high dimensional growing self organizing map with randomness HDGSOMr Summary This paper presents an algorithm based on the Growing Self Organizing Map (GSOM) called the High Dimensional Growing Self Organizing Map with Randomness (HDGSOMr) that can cluster massive high dimensional data efficiently. The original GSOM algorithm is altered to accommodate for the issues related to massive high dimensional data. These modifications are presented in detail with experimental results of a massive real-world dataset. Notes Book title: Neural information processing Language eng DOI 10.1007/11893257_90 Field of Research 080499 Data Format not elsewhere classified Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences HERDC Research category C1 Refereed article in a scholarly journal Copyright notice ©2006, Springer-Verlag Berlin Heidelberg Persistent URL http://hdl.handle.net/10536/DRO/DU:30009051 Document type: Journal Article Collections: Faculty of Science, Engineering and Built Environment School of Engineering and Information Technology Related Links Link Description Connect to published 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. Versions Version Filter Type Mon, 09 Feb 2009, 10:16:39 EST Fri, 28 May 2010, 12:01:50 EST Mon, 30 Apr 2012, 17:01:30 EST Fri, 10 Oct 2014, 08:23:06 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 3 times in Scopus Search Google Scholar Access Statistics: 519 Abstract Views, 0 File Downloads  -  Detailed Statistics Created: Mon, 13 Oct 2008, 15:49:43 EST