Algorithm for data clustering in pattern recognition problems based on quantum mechanics.

Abstract

We propose a novel clustering method that is based on physical intuition derived from quantum mechanics. Starting with given data points, we construct a scale-space probability function. Viewing the latter as the lowest eigenstate of a Schrödinger equation, we use simple analytic operations to derive a potential function whose minima determine cluster centers. The method has one parameter, determining the scale over which cluster structures are searched. We demonstrate it on data analyzed in two dimensions (chosen from the eigenvectors of the correlation matrix). The method is applicable in higher dimensions by limiting the evaluation of the Schrödinger potential to the locations of data points.

Extracted Key Phrases

3 Figures and Tables

Statistics

01020'02'04'06'08'10'12'14'16
Citations per Year

123 Citations

Semantic Scholar estimates that this publication has 123 citations based on the available data.

See our FAQ for additional information.

Cite this paper

@article{Horn2002AlgorithmFD, title={Algorithm for data clustering in pattern recognition problems based on quantum mechanics.}, author={David Horn and Assaf Gottlieb}, journal={Physical review letters}, year={2002}, volume={88 1}, pages={018702} }