Density-based clustering with topographic maps

Author

Van Hulle, Marc M.

Author_Institution

Lab. voor Neuro- en Psychofysiologie, Katholieke Univ., Leuven, Belgium

Volume

Issue

fYear

1999

fDate

1/1/1999 12:00:00 AM

Firstpage

204

Lastpage

207

Abstract

A new unsupervised competitive learning rule is introduced, called the kernel-based maximum entropy learning rule (kMER), for equiprobabilistic topographic map formation. The application envisaged is density-based clustering. An empirical study is conducted to compare the clustering performance of kMER with that of a number of other unsupervised competitive learning rules

Keywords

Bayes methods; maximum entropy methods; network topology; neural nets; pattern recognition; probability; unsupervised learning; Bayes method; competitive learning; density-based clustering; equiprobabilistic map; kernel-based maximum entropy learning rule; nonparametric density estimation; probability; topographic maps; topology; unsupervised learning; Bayesian methods; Clustering algorithms; Cost function; Density functional theory; Entropy; Neural networks; Neurons; Radio frequency; Resonance; Statistical distributions;

fLanguage

English

Journal_Title

Neural Networks, IEEE Transactions on

Publisher

ieee

ISSN

1045-9227

Type

jour

DOI

10.1109/72.737510

Filename

737510

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1458323