A clustering technique for random data classification
Carlos Alberto Reyes, Malek Adjouadi · 2002
This paper introduces a new clustering technique for random data classification based on an enhanced version of the Voronoi diagram. This technique is optimized to deal in the best way possible with data distributions which experience overlap in their geometric constructs. A thorough analysis is provided in dealing with the dilemmas imposed by the regions of overlap over the prospect of proper data classification. A mathematical framework is given in view of this enhanced analysis and with respect to the description of real-world data through superposition of Gaussian distributions. Computer results prove the soundness of this clustering technique.