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

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Title: CQoCO: A measure for comparative quality of coverage and organization for self-organizing maps
Authors: Beaton, Derek1, Valova, Iren ivalova@umassd.edu, MacLean, Daniel1
Source: Neurocomputing. Jun2010, Vol. 73 Issue 10-12, p2147-2159. 13p.
Subjects: Self-organizing maps, Comparative studies, Geometric quantization, Error analysis in mathematics, Artificial neural networks, Cluster analysis (Statistics)
Abstract: 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. [Copyright &y& Elsevier]
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Database: Engineering Source
Description
Abstract: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. [Copyright &y& Elsevier]
ISSN:09252312
DOI:10.1016/j.neucom.2010.02.004