Finding All Frequent Patterns Starting from the Closure

Efficient discovery of frequent patterns from large databases is an active research area in data mining with broad applications in industry and deep implications in many areas of data mining. Although many efficient frequent-pattern mining techniques have been developed in the last decade, most of them assume relatively small databases, leaving extremely large but realistic datasets out of reach. A practical and appealing direction is to mine for closed itemsets. These are subsets of all frequent patterns but good representatives since they eliminate what is known as redundant patterns. In this paper we introduce an algorithm to discover closed frequent patterns efficiently in extremely large datasets. Our implementation shows that our approach outperforms similar state-of-the-art algorithms when mining extremely large datasets by at least one order of magnitude in terms of both execution time and memory usage.