Abstract: Current technology for frequent itemset mining mostly applies to the data stored in a single transaction database. This paper presents a novel algorithm MultiClose for frequent itemset mining in data warehouses. MultiClose respectively computes the results in single dimension tables and merges the results with a very efficient approach. Close itemsets technique is used to improve the performance of the algorithm. The authors propose an efficient implementation for star schemas in which their algorithm outperforms state-of-the-art single-table algorithms.

Key words: Frequent itemset, Close itemset, Star schema, Dimension table, Fact table

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