Building Virtual Reality Spaces for Visual Data Mining with Hybrid Evolutionary-Classical Optimization: Application to Microarray Gene Expression Data

J.J. Valdés (Canada)


data mining, virtual reality, hybrid optimization.


Visual data mining via the construction of virtual re ality spaces for the representation of data and knowledge, involves the solution of optimization problems. This pa per introduces a hybrid technique based on particle swarm optimization (PSO) combined with classical optimization methods. This aproach is applied to very high dimensional data from microarray gene expression experiments in order to understand the structure of both raw and processed data. Experiments with data sets corresponding to Alzheimer's disease show that high quality visual representations can be obtained by combining PSO with classical optimization methods. The behavior of some of the parameters control ling the swarm evolution is also studied.

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