Clustering by SOM (self-organising maps), MST (minimal spanning tree) and MCP (modified counter-propagation)

Author

Obu-Cann, K. ; Iwamoto, K. ; Tokutaka, H. ; Fujimura, K.

Author_Institution

Dept. of Electr. & Electron. Eng., Tottori Univ., Japan

Volume

3

fYear

1999

fDate

1999

Firstpage

986

Abstract

Evaluation of the cluster classification generated by a SOM is usually done by human eye. Due to the qualitative nature of this experiment, the evaluator may either overestimate or underestimate the number of clusters formed on the map. With this approach, the exact number of clusters generated by the map cannot be confirmed because of the misinterpretation of the grey-level expression. This paper presents the use of MSTs and MCP in cluster classification and reports on the application of a SOM to the chemical analysis of alloys

Keywords

alloys; backpropagation; chemical analysis; chemistry computing; minimisation; pattern classification; pattern clustering; self-organising feature maps; trees (mathematics); alloys; chemical analysis; cluster classification; cluster estimation; grey-level expression misinterpretation; minimal spanning tree; modified counter-propagation; qualitative evaluation; self-organising maps; Chemical analysis; Counting circuits; Electrons; Humans; Kinetic energy; Laboratories; Lattices; Multidimensional systems; Spectroscopy; X-ray scattering;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Information Processing, 1999. Proceedings. ICONIP '99. 6th International Conference on

Conference_Location

Perth, WA

Print_ISBN

0-7803-5871-6

Type

conf

DOI

10.1109/ICONIP.1999.844670

Filename

844670