CQoCO: A measure for comparative quality of coverage and organization for self-organizing maps

Authors:
Derek Beaton;Iren Valova;Daniel MacLean
Affiliations:
Computer and Information Science, University of Massachusetts Dartmouth, USA;Computer and Information Science, University of Massachusetts Dartmouth, USA;Computer and Information Science, University of Massachusetts Dartmouth, USA
Venue:
Neurocomputing
Year:
2010

Citing 7
Cited 4

Self-organizing maps

Self-organizing maps
Measures for the organization of self-organizing maps

Self-Organizing neural networks
Algorithms for Reporting and Counting Geometric Intersections

IEEE Transactions on Computers
Growing mechanisms and cluster identification with TurSOM

IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
A parallel growing architecture for self-organizing maps with unsupervised learning

Neurocomputing
Topology preservation in self-organizing feature maps: exact definition and measurement

IEEE Transactions on Neural Networks
Quantifying the neighborhood preservation of self-organizing feature maps

IEEE Transactions on Neural Networks

TurSOM: a turing inspired self-organizing map

IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Growing mechanisms and cluster identification with TurSOM

IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
A programmable triangular neighborhood function for a Kohonen self-organizing map implemented on chip

Neural Networks
TurSOM: A paradigm bridging Turing's unorganized machines and self-organizing maps demonstrating dual self-organization

Neurocomputing

Quantified Score

Hi-index	0.01

Visualization

Abstract

In this paper we introduce a comprehensive measure of quality of coverage and organization in self-organizing maps. The new measure, named CQoCO, combines self-organizing criteria as outlined by Polani, by measuring how, and where neurons are mapped in space and how they are organized. The result is an all-inclusive tool to account for the required properties of a well-organized SOM-untangled coverage of inputs only, reflecting the density of the input space and outlining the natural clusters in the data. The proposed measure has been tested on the traditional version of SOM as well as on a growing version, as proposed by the authors-ParaSOM. While the patterns are simple, the tests also cover a comparison between CQoCO and the measures of quantization error and topological error. The conclusion becomes evident with the progress of the experiments-CQoCO provides an across-the-board measurement of the quality of coverage and organization in SOMs.