Mining frequent XML documents using FFP

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Amar Nayak


XML has become very popular for representing semi structured data and a standard for data exchange over the web. Mining XML data from the web is becoming increasingly important. To date, the famous Apriori algorithm to mine any XML document for association rules without any pre-processing or post-processing has been implemented using only the XQuery language which is costly. But the algorithm only can mine the set of items that can be written a path expression for. However, the structure of the XML data can be more complex and irregular than that. Consequently, it is difficult to identify the mining context. This paper propose that extracting association rules from XML documents without any preprocessing or postprocessing using XML query language XQuery is possible and analyze the XQuery implementation of the efficient First Frequent method-tree based mining method, First Frequent Pattern-growth, for mining the complete set of frequent patterns by pattern fragment growth. First Frequent Pattern-tree based mining adopts a pattern fragment growth method to avoid the costly generation of a large number of candidate sets and a partition-based, divide-and-conquer method is used. In addition, we suggest features that need to be added into XQuery in order to make the implementation of the First Frequent Pattern growth more efficient.




Keywords: Data Mining, XML, XPATH, XQuery, SDST


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