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Federico Marini and Beata Walczak Finding relevant clustering directions in high-dimensional data using Particle Swarm Optimization Journal of Chemometrics 25

Version of Record online: 22 OCT 2010 | DOI: 10.1002/cem.1345

A method based on Particle Swarm Optimization (PSO) is proposed and described for finding subspaces that carry meaningful information about the presence of groups in high-dimensional data sets. The method was successfully used on two simulated data set and on a real matrix coming from the analysis of genomic microarrays.

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