Mining Chains of Relations Aristides Gionis, Foto Afrati, Gautam Das, Heikki Mannila, Taneli Mielikainen, and Panayiotis Tsaparas   View Paper (PDF)   Return to Session 16: Learning Techniques II Abstract Traditional data mining applications consider the problem of mining a single relation between two attributes. For example, in a scientific bibliography database, authors are related to papers, and we may be interested in discovering association rules between authors. However, in real life, we often have multiple attributes related though chains of relations. For example, authors write papers, and papers concern one or more topics. Mining such relational chains poses additional challenges. In this paper we consider the following problem: given a chain of two relationsR©û(A, P) and R©ü(P, T) we want to find selectors for the objects in T such that the projected relation between A and P satisfies a specific property. The motivation for our approach is that a given property might not hold on the whole dataset, but it might hold when projecting the data on a selector set. We discuss various algorithms and we examine the conditions under which the apriori technique can be used. We experimentally demonstrate the effectiveness of our methods. ©2006 Association for Computing Machinery