Abstract
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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Ribeiro, M.X., Pires Vieira, M.T. (2004). A New Approach for Mining Association Rules in Data Warehouses. In: Christiansen, H., Hacid, MS., Andreasen, T., Larsen, H.L. (eds) Flexible Query Answering Systems. FQAS 2004. Lecture Notes in Computer Science(), vol 3055. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-25957-2_9
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DOI: https://doi.org/10.1007/978-3-540-25957-2_9
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