Abstract
Self Organizing Features Maps are used for a variety of tasks in visualization and clustering, acting to transform data from a high-dimensional original feature space to a (usually) two-dimensional grid. SOFMs use a similarity metric in the input space, and this composes individual feature differences in a way that is not always desirable. This paper introduces the concept of a Pareto SOFM, which partitions features into groups, defines separate metrics in each partition, and retrieves a set of prototypes that trade off matches in different partitions. It is suitable for a wide range of exploratory tasks, including visualization and clustering....