Title :
Density-based clustering with topographic maps
Author :
Van Hulle, Marc M.
Author_Institution :
Lab. voor Neuro- en Psychofysiologie, Katholieke Univ., Leuven, Belgium
fDate :
1/1/1999 12:00:00 AM
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;
Journal_Title :
Neural Networks, IEEE Transactions on
