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.

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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
Keyword(s) growing self organizing map
high dimensional growing self organizing map with randomness
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
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Document type: Journal Article
Collection: School of Engineering and Information Technology
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