Initialization of adaptive parameters in neural networks is of crucial
importance to the speed of convergence of the learning procedure. Methods
of initialization for the density networks are reviewed and two new
methods, based on decision trees and dendrograms, presented. These two
methods were applied in the Feature Space Mapping framework to artificial
and real world datasets. Results show superiority of the dendrogram-based
method including rotation.