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Given two sets of training samples, general method is to estimate the densityfunction and classify the test sample according to higher values of estimateddensities. Natural way to estimate the density should be histogram tending tofrequency curve. But nature of different amount of data accumulation atdifferent points, obstruct us to take common interval length in drawinghistogram. Here we followed a method similar to drawing histogram with anintention to density estimation and classification, but without doingarbitrarily we considered diagram of the scattered plot and has obtainedestimate of density based on areas of the Voronoi cells. But area of arbitraryVoronoi cell may be difficult to find out particularly in two and higherdimensions, and also in manifolds. For this reason we estimate the area of theVoronoi cell by drawing many independent uniform variates covering the supportof the training samples. Please note that though we have in mind densityestimation and classification problem in higher dimension and in typicalmanifolds, but our simulations and algebraic calculations have been mostlyshown for densities on real line. We expect in higher dimension and inmanifolds the work will be in similar lines and plan to do in near future. Alsowe expect discrete cases can be handled in slightly different but similar lineswhich are also left for future works.

Classification of Population Using Voronoi Area Based Density Estimation