Genetic-based clustering neural networks and applications

Author

Sun, Chengyi ; Chao, Hongxing ; Sun, Yan

Author_Institution

Comput. Center, Taiyuan Univ. of Technol., China

Volume

1

fYear

1997

fDate

28-31 Oct 1997

Firstpage

439

Abstract

Maximum-likelihood clustering neural networks (MLCNNs) have some prominent advantages over many other clustering algorithms. However, there is an obvious problem in MLCNNs, namely that the initial cluster centers have a great influence on the clustering results. In this paper, genetic algorithms are combined with MLCNNs to solve the problem of the selection of initial cluster centers so that the MLCNNs can give optimal clustering results. The genetic-based MLCNNs are applied to the segmentation and understanding of images through connected components and to the analysis of stock market data. In these applications, the genetic-based MLCNNs play important roles and lead to excellent results

Keywords

data analysis; financial data processing; genetic algorithms; image segmentation; maximum likelihood estimation; neural nets; pattern recognition; stock markets; clustering algorithms; connected components; genetic algorithms; image segmentation; image understanding; initial cluster center selection; maximum-likelihood clustering neural networks; optimal clustering results; stock market data analysis; Application software; Chaos; Clustering algorithms; Computer science education; Genetic algorithms; Image analysis; Image segmentation; Neural networks; Stock markets; Sun;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Processing Systems, 1997. ICIPS '97. 1997 IEEE International Conference on

Conference_Location

Beijing

Print_ISBN

0-7803-4253-4

Type

conf

DOI

10.1109/ICIPS.1997.672819

Filename

672819