Mining Stars with FP-Growth: a Case Study on Bibliographic Data

Traditional data mining approaches look for patterns in a single table, while multirelational data mining aims for identifying patterns that involve multiple tables. In recent years, the most common mining techniques have been extended to the multirelational context, but there are few dedicated to deal with data stored following the multi-dimensional model, in particular the star schema. These schemas are composed of a central huge fact table linking a set of small dimension tables. Joining all the tables before mining may not be a feasible solution due to the usual massive number of records. This work proposes a method for mining frequent patterns on data following a star schema that does not materialize the join between the tables. As it extends the algorithm FP-Growth, it constructs an FP-Tree for each dimension and then combines them through the records in the fact table to form a super FP-Tree. This tree is then mined with FP-growth to find all frequent patterns. The paper presents a case study on bibliographic data, comparing efficiency and scalability of our algorithm against FPGrowth.