title: Online clustering algorithms.
abstract: We introduce a set of clustering algorithms whose performance function is such that the algorithms overcome one of the weaknesses of K-means, its sensitivity to initial conditions which leads it to converge to a local optimum rather than the global optimum. We derive online learning algorithms and illustrate their convergence to optimal solutions which K-means fails to find. We then extend the algorithm by underpinning it with a latent space which enables a topology preserving mapping to be found. We show visualisation results on some standard data sets.
authors.affiliation: The University of Paisley, Scotland, UK. wesam.barbakh@paisley.ac.uk
authors.forename: Wesam
authors.initials: W
authors.lastname: Barbakh
authors.forename: Colin
authors.initials: C
authors.lastname: Fyfe
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D016000:Cluster Analysis; D016247:Information Storage and Retrieval