Pattern Mining on Stars with FP-Growth

Abstract. Most existing data mining (DM) approaches look for patterns in a single table. Multi-relational DM approaches, on the other hand, look for patterns that involve multiple tables. In recent years, the most common DM techniques have been extended to the multi-relational case, but there are few dedicated to star schemas. These schemas are composed of a central fact table, linking a set of dimension tables, and joining all the tables before mining may not be a feasible solution. This work proposes a method for frequent pattern mining in a star schema based on FP-Growth. It does not materialize the entire join between the tables. Instead, it constructs an FP-Tree for each dimension and then combines them to form a super FP-Tree, that will serve as input to FP-Growth.

• Type: Journal
• Year: 2010
• Where: MDAI
• Authors: Andreia Silva, Cláudia Antunes
• Keywords: Artificial Intelligence, Common Dm Techniques, MDAI 2010, Mining, Most Existing Data