Interactive exploratory data analysis

Abstract

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 definite either unknown a priori or difficult to formalize. The first 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. In a second example, we show how a user can interactively evolve an auditory display of cluster data. In this example, we use IEC with genetic programming to evolve a mapping of data to sound functions in order to sonify qualities of data clusters.