Distance Based Approaches to Relational Learning and Clustering

Abstract

Within data analyis, disstance-based methods have always been very popular. Such methods assume that it is possible to compute for each pair of objects in a domain their mutual distance (or similarity). In a distance-based setting, many of the tasks usually considered in data mining can be carried out in a surprisingly simple yet powerful way. In this chapter, we give a tutorial introduction to the use of distance-based methods for relational representations, concentrating in particular on predictive learning and clustering. We describe in detail one relational distance measure that has proven very successful in applications, and introduce three systems that actually carry out relational distance-based learning and clustering: RIBL2, RDBC, and FORC. We also present a detailed case study of how these three systems were applied to a domain from molecular biology.