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K-nearest neighbors (KNN) method is used in many supervised learningclassification problems. Potential Energy (PE) method is also developed forclassification problems based on its physical metaphor. The energy potentialused in the experiments are Yukawa potential and Gaussian Potential. In thispaper, I use both applet and MATLAB program with real life benchmark data toanalyze the performances of KNN and PE method in classification problems. Theresults show that in general, KNN and PE methods have similar performance. Inparticular, PE with Yukawa potential has worse performance than KNN when thedensity of the data is higher in the distribution of the database. When theGaussian potential is applied, the results from PE and KNN have similarbehavior. The indicators used are correlation coefficients and informationgain.

Comparing K-Nearest Neighbors and Potential Energy Method in  classification problem. A case study using KNN applet by E.M. Mirkes and real  life benchmark data sets