Growing hierarchical self organising map (GHSOM) toolbox: visualisations and enhancements

Author

Chan, Alvin ; Pampalk, Elias

Author_Institution

Dept. of Eng., Univ. of Aberdeen, UK

Volume

5

fYear

2002

fDate

18-22 Nov. 2002

Firstpage

2537

Abstract

The Growing Hierarchical Self Organising Map (GHSOM) presents a method of dynamically modeling the data set that is presented. To a certain extent the GHSOM provides a solution to determine the size of the SOM needed, which is done through a growing fashion of neurons. In our development of the GHSOM Toolbox for Matlab presented in this paper, we have discovered that the GHSOM algorithm also provides a visualisation advantage of having the ability of presenting classes and sub-classes of similar data. We also propose two enhancements to the algorithm: (1) Usage of cumulative quantisation errors for better resolution in the growth process and (2) Tidier algorithm for initialisation of sub layer neurons for orientation.

Keywords

data models; data visualisation; learning (artificial intelligence); self-organising feature maps; GHSOM Toolbox for Matlab; algorithm enhancements; cumulative quantisation errors; data clusters; dynamic modeling; growing fashion of neurons; growing hierarchical self organising map; sublayer orientation; tidier algorithm; training process; visualisation; Artificial intelligence; Data engineering; Data visualization; Equations; Mathematical model; Neurons; Quantization;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Information Processing, 2002. ICONIP '02. Proceedings of the 9th International Conference on

Print_ISBN

981-04-7524-1

Type

conf

DOI

10.1109/ICONIP.2002.1201952

Filename

1201952