Support driven opportunistic aggregation for generalized itemset extraction
Elena Baralis, Luca Cagliero, Tania Cerquitelli, Vincenzo D’Elia, Paolo Garza · 2010
Association rule extraction is a widely used exploratory technique which has been exploited in different contexts (e.g., biological data, medical images). However, association rule extraction, driven by support and confidence constraints, entails (i) generating a huge number of rules which are difficult to analyze, or (ii) pruning rare itemsets, even if their hidden knowledge might be relevant. To address the above issues, this paper presents a novel frequent itemset mining algorithm, called GENIO (GENeralized Itemset DiscOverer), to analyze correlation among data by means of generalized itemsets, which provide a powerful tool to efficiently extract hidden knowledge, discarded by previous approaches. The proposed technique exploits a (user provided) taxonomy to drive the pruning phase of the extraction process. Instead of extracting itemsets for all levels of the taxonomy and post-pruning them, the GenIO algorithm performs a support driven opportunistic aggregation of itemsets. Generalized itemsets are extracted only if itemsets at a lower level in the taxonomy are below the support threshold. Experiments performed in the network traffic domain show the efficiency and the effectiveness of the proposed algorithm.