Clustering massive high dimensional data with dynamic feature maps

Autoria(s): Amarasiri, Rasika; Alahakoon, Damminda; Smith-Miles, Kate
Data(s)

01/01/2006
Resumo

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.<br />
Identificador

http://hdl.handle.net/10536/DRO/DU:30009051
Idioma(s)

eng
Publicador

Springer-Verlag
Relação

http://dro.deakin.edu.au/eserv/DU:30009051/n20061169.pdf

http://dx.doi.org/10.1007/11893257_90
Direitos

2006, Springer-Verlag Berlin Heidelberg
Palavras-Chave #growing self organizing map #GSOM #high dimensional growing self organizing map with randomness #HDGSOMr
Tipo

Journal Article
Acesso ao item digital