What is it about?
ata clustering is a vital tool for data analysis. This work shows that some existing useful methods in data clustering are actually based on quantum mechanics and can be assembled into a powerful and accurate data clustering method where the efficiency of computational quantum chemistry eigenvalue methods is therefore applicable. These methods can be applied to scientific data, engineering data and even text.
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Why is it important?
This means that the considerable body of computational quantum mechanics (and quantum chemistry) including eigenvalue solvers can be fruitfully used for Data Analysis. E.g. row-stochastic Markov matrices are used as Transition matrices and within the Meila-Shi algorithm for dimensional reduction.
Perspectives
The possibilities for this kind of data analysis are considerable and there has already been a number of applications. See Wikipedia's site on "Quantum Clustering"
Dr Tony Cyril Scott
RWTH-Aachen University
Read the Original
This page is a summary of: Data Clustering with Quantum Mechanics, Mathematics, January 2017, MDPI AG,
DOI: 10.3390/math5010005.
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