Clustering with kernel-based equiprobabilistic topographic maps

A new unsupervised competitive learning rule is introduced which performs equiprobabilistic topographic map formation. The receptive fields are overlapping radially-symmetric kernels of which the radii are adapted to the local input density, together with the weight vectors which define the kernel centers. The application envisaged is density-based clustering.