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Chapter 13

Discovering Higher Level Correlations from XML Data

Luca Cagliero

Politecnico di Torino, Italy

Tania Cerquitelli

Politecnico di Torino, Italy

Paolo Garza

Politecnico di Milano, Italy

Abstract

Association rule extraction is a widely used exploratory technique that allows the identification of hidden correlations among data. The problem of generalized association rule mining, originally introduced in the context of market basket analysis, exploits a taxonomy to drive the mining activity with the aim at discovering associations between data items at any level of the taxonomy. Since XML has become a standard for representing and exchanging information, the extraction of association rules from XML data is becoming appealing as it allows for identifying ...

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