R.P. Gopalan and Y.G. Sucahyo (Australia)
Knowledge Discovery, Data Mining, Association Rules, Frequent Itemsets
Discovering association rules by identifying relationships among sets of items in a transaction database is an important problem in Data Mining. Finding frequent itemsets is computationally the most expensive step in association rule discovery and therefore it has attracted significant research attention. In this paper, we describe a more efficient algorithm for mining complete frequent itemsets from typical data sets. We use a compressed prefix tree and our algorithm extracts the frequent itemsets directly from the tree. We present performance comparisons of our algorithm against the fastest Apriori algorithm, Eclat, and FP-Growth. These results show that our algorithm outperforms other algorithms on several widely used test data sets.
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