An improving pruning technique with restart for the Kohonen self-organizing feature map

Author

De Castro, Leandro Nunes ; Von Zuben, Fernando J.

Author_Institution

Dept. of Comput. Eng. & Ind. Autom., State Univ. of Campinas, Brazil

Volume

3

fYear

1999

fDate

1999

Firstpage

1916

Abstract

Presents a pruning technique developed for the one-dimensional Kohonen self-organizing feature map (SOM) to be applied in clustering and classification problems. Its innovative aspect is the combined proposition of a penalty term, a clustering measure, a delayed pruning activation and a restarting phase. The proposed algorithm (PSOM) always guides to a reduced architecture capable of representing the data set. We compare the PSOM with the original SOM applying them to three different classification problems. The results show that the PSOM is able to present superior performance in all cases

Keywords

neural net architecture; pattern classification; pattern clustering; self-organising feature maps; unsupervised learning; Kohonen self-organizing feature map; clustering measure; delayed pruning activation; penalty term; pruning technique; reduced architecture; restarting phase; Automation; Clustering algorithms; Computer industry; Data analysis; Delay; Phase measurement; Signal mapping; Signal processing algorithms; Speech recognition; Unsupervised learning;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1999. IJCNN '99. International Joint Conference on

Conference_Location

Washington, DC

ISSN

1098-7576

Print_ISBN

0-7803-5529-6

Type

conf

DOI

10.1109/IJCNN.1999.832674

Filename

832674