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The inherent flexibilities of XML in both structure and semantics makes mining from XML data a complex task with more challenges compared to traditional association rule mining in relational databases. This paper proposes a new model for the effective extraction of generalized association rules form a XML document collection. This paper directly uses frequent subtree mining techniques in the discovery process and do not ignore the tree structure of data in the final rules. The frequent subtrees based on the user provided support are split to complement subtrees to form the rules. This paper explains the model within multi-steps from data preparation to rule generation.
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