Enhanced Associative Classification of XML Documents Supported by Semantic Concepts

A novel approach based on supervised classification has been proposed to classify a given collection of XML documents based on rule based classifier by semantically enriched structure and content features. The proposed methodology overcomes the drawbacks of the existing technologies by accomplishing the classification by utilizing not only the structure and content features but also context. It applies ontological information into structural and content based features from the XML documents and transforms it into transaction formats onto which FP-growth algorithm is executed to generate association rules. An associative classifier is constructed by eliminating irrelevant rules from the generated association rule.