Distance Based Approaches to Relational Learning and Clustering

Mathias Kirsten, Stefan Wrobel, and Tamas Horvath. In Saso Dzeroski and Nada Lavrac, editors, Relational Data Mining, pages 213--232. Springer-Verlag, September 2001. More behind this link.

Abstract

Within data analysis, distance-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: RIBLB, RDBC and FORC. We also present a detailed case study of how these three systems were applied to a domain from molecular biology.

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M Kirsten, Mathias.Kirsten@gmd.de,

S Wrobel, Stefan.Wrobel@gmd.de,