The self-organizing map tree (SOMT) for nonlinear data causality prediction

Authors:
Younjin Chung;Masahiro Takatsuka
Affiliations:
ViSLAB, The School of IT, The University of Sydney, NSW, Australia;ViSLAB, The School of IT, The University of Sydney, NSW, Australia
Venue:
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
Year:
2011

Citing 4
Cited 0

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Abstract

This paper presents an associated visualization model for the nonlinear and multivariate ecological data prediction processes. Estimating impacts of changes in environmental conditions on biological entities is one of the required ecological data analyses. For the causality analysis, it is desirable to explain complex relationships between influential environmental data and responsive biological data through the process of ecological data predictions. The proposed Self-Organizing Map Tree utilizes Self-Organizing Maps as nodes of a tree to make association among different ecological domain data and to observe the prediction processes. Nonlinear data relationships and possible prediction outcomes are inspected through the processes of the SOMT that shows a good predictability of the target output for the given inputs.