Consensus clustering for dimensionality reduction

Dimensionality reduction continues to be a challenging problem with huge amounts of data being generated in the domains of bio-informatics, social networks etc. We propose a novel dimensionality reduction algorithm based on the idea of consensus clustering using genetic algorithms. Classification is used as validation and the algorithm is evaluated on benchmark data sets of dimensionality ranging from 8 to 617 features. The results are on par with the latest approaches proposed in the literature.