Name
Title | ||
|---|---|---|
CQoCO: A measure for comparative quality of coverage and organization for self-organizing maps |
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. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1016/j.neucom.2010.02.004 | Neurocomputing |
Keywords | Field | DocType |
traditional version,topological error,new measure,input space,self-organizing map,quantization error,proposed measure,comparative quality,comprehensive measure,well-organized som-untangled coverage,across-the-board measurement,self organization | Data mining,Cluster (physics),Self-organizing map,Artificial intelligence,Quantization (signal processing),Machine learning,Mathematics | Journal |
Volume | Issue | ISSN |
73 | 10-12 | Neurocomputing |
Citations | PageRank | References |
22 | 1.22 | 9 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Derek Beaton | 1 | 47 | 5.52 |
| Iren Valova | 2 | 136 | 25.44 |
| Daniel MacLean | 3 | 113 | 11.82 |