C.: F2g: Efficient discovery of full-patterns

Abstract. An increasing number of biomedical tasks, such as pattern-based biclustering, require the disclosure of the transactions (e.g. genes) that support each pattern (e.g. expression profiles). The discovery of pat-terns with their supporting transactions, referred as full-pattern mining, has been solved recurring to extensions over Apriori and vertical-based algorithms for frequent itemset mining. Although pattern-growth alter-natives are known to be more efficient across multiple biological datasets, there are not yet adaptations for the efficient delivery of full-patterns. In this paper, we propose a pattern-growth algorithm able to discover full-patterns with heightened efficiency and minimum memory overhead. Results confirm that for dense datasets or low support thresholds, a common requirement in biomedical settings, this method can achieve significant performance improvements against its peers.