Scaling data mining activities on very large datasets

TL;DRAbstract

This thesis addresses the issue of enhancing the scalability of data mining techniques, with specific emphasis on association rule and frequent itemset mining. In particular, it proposes a scalable itemset mining approach relying on (i) a persistent (disk-based) representation of the transactional data, (ii) ad-hoc data retrieval techniques, and (iii)~strategies for the integration of existing itemset mining algorithms. A parallel design based on the same approach, to perform itemset extraction in a parallel and/or distributed environment, is also described. To address the manageability of frequent itemsets, a concise disk-based representation, with a set of querying techniques, is proposed. This work has been preliminarly validated in the Semantic Web domain, to identify semantic relationships from textual collections with a semi-automatic approach. As a minor topic, the extracion of frequent itemsets from streams of data, modelled as a set of transactional data windows, has also been

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