Implementing multi-relational mining with relational database systems

Abstract

Multi-relational data mining (MRDM) is to enumerate frequently appeared patterns in data, the patterns which are appeared not only in a relational table but over a collection of tables. Although a database usually consists of many relational tables, most of data mining approaches treat patterns only on a table. An approach based on ILP (inductive logic programming) is a promising approach and it treats patterns on many tables. Pattern miners based on the ILP approach produce expressive patterns and are wide-applicative but computationally expensive because the miners search among large pattern space. We have been proposing a mining algorithm called MAPIX[3]. MAPIX has an advantage that it constructs patterns by combining atomic properties extracted from sampled examples. By restricting patterns into combinations of the atomic properties it gained efficiency compared with conventional algorithms including WARMR[1,2]. In order to scale MAPIX to treat large dataset on standard relational database systems, this paper studies implementation issues. © 2009 Springer Berlin Heidelberg.