Integrating association rule mining with relational database systems: alternatives and implications
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Mining Association Rules in Multiple Relations
ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
A Mining Algorithm Using Property Items Extracted from Sampled Examples
Inductive Logic Programming
Implementing Multi-relational Mining with Relational Database Systems
KES '09 Proceedings of the 13th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems: Part II
Relational pattern mining based on equivalent classes of properties extracted from samples
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
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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. MAPIX[2] 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 other algorithms. In order to scale MAPIX to treat large dataset on standard relational database systems, this paper studies implementation issues.