A New Approach for Mining Association Rules in Data Warehouses

Interesting patterns can be revealed by applying knowledge discovery processes in data warehouses. However, the existing data mining techniques only allow one to extract patterns from a single fact table of a data warehouse. Since each fact table contains data about a subject, the existing techniques do not allow multiple subjects of a data warehouse to be related. In this paper, we propose a new technique for mining association rules in a data warehouse, which allows items from multiple subjects of a data warehouse to be related. The rules mined through this technique are called multifact association rules because they relate items from multiple fact tables. We propose a new efficient algorithm called Connection to mine such rules. The proposed algorithm can process each fact table in parallel, resulting in improved performance.

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