Experiments on mapping techniques for exploratory pattern analysis

Abstract We describe two computer-based experiments evaluating the effectiveness of several mapping techniques for exploratory pattern analysis. The first experiment compares various mappings and classical clustering techniques as aids to people whose objective is to find clusters in the data. The second experiment evaluates the effectiveness of two-dimensional displays produced by analytic mappings for people designing linear and piecewise linear classifiers. The performance of the classifiers designed by the people aided by these displays is compared with automatically trained classifiers. Based on these experiments we selected three best mapping methods. Even the untrained users who took part in our experiments achieved very good results with the aid of these best mappings. In fact, these results were superior by a significant margin to those obtained from renowned classical pattern recognition procedures. Another valuable result of our experiments is that they allowed us to identify the sets of parameters most often used by the participants and, consequently, suggest guidelines for the best use of mapping techniques.