Exploratory data analysis with interactive evolution

We illustrate with two simple examples how Interactive Evolutionary Computation (IEC) can be applied to Exploratory Data Analysis (EDA). IEC is particularly valuable in an EDA context because the objective function is by definition either unknown a priori or difficult to formalize. The first example involves what is probably the simplest possible transformation of data: linear projections. While the concept of linear projections is simple to grasp, in practice finding the appropriate two-dimensional projection that reveals important features of high-dimensional data is no easy task. We show how IEC can be used to quickly find the most informative linear projection(s). In another, more complex example, IEC is used to evolve the "true" metric of attribute space. Indeed, the assumed distance function in attribute space strongly conditions the information content of a two-dimensional display of the data, regardless of the dimension reduction approach. The goal here is to evolve the attribute space distance function until "interesting" features of the data are revealed when a clustering algorithm is applied.