Discovering Associations in XML Data

—Knowledge inference from semi-structured data can utilize frequent sub structures, in addition to frequency of data items. In fact, the working assumption of the present study is that frequent sub-trees of XML data represent sets of tags (objects) that are meaningfully associated. A method for extracting frequent sub-trees from XML data is presented. It uses thresholds on frequencies of paths and on the multiplicity of paths in the data. The frequent sub-trees are extracted and counted in a procedure that has  ¢¡¤£¦¥¨§ complexity. The data content of the extracted sub-trees, in the form of attribute values, is cast in tabular form. This enables a search for associations in the extracted data. Thus, the complete procedure uses structure and content to extract association rules from semistructured data. A large industrial example is used to demonstrate the operation of the proposed method. x

• Type: Conference
• Year: 2002
• Where: WISE
• Authors: Amnon Meisels, Michael Orlov, Tal Maor
• Keywords: Frequent Sub Structures, Frequent Sub-trees, Internet Technology, WISE 2002, XML Data