Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/39709
Title: A framework for efficient association rule mining in XML data
Authors: Zhang, J.
Liu, H.
Ling, T.W. 
Bruckner, R.M.
Min Tjoa, A.
Keywords: Association rule mining
Concept generalization
Data transformation and indexing
Metapatterns
XML data
Issue Date: 2006
Source: Zhang, J.,Liu, H.,Ling, T.W.,Bruckner, R.M.,Min Tjoa, A. (2006). A framework for efficient association rule mining in XML data. Journal of Database Management 17 (3) : 19-40. ScholarBank@NUS Repository.
Abstract: In this article, we propose a framework, called XAR-Miner, for mining ARs from XML documents efficiently. In XAR-Miner, raw data in the XML document first are preprocessed to transform either to an Indexed XML Tree (IX-tree) or to Multirelational Databases (Multi-DB), depending on the size of the XML document and the memory constraint of the system, for efficient data selection and AR mining. Concepts that are relevant to the AR mining task are generalized to produce generalized metapatterns. A suitable metric is devised for measuring the degree of concept generalization in order to prevent undergeneralization or overgeneralization. Resulting generalized metapatterns are used to generate large ARs that meet the support and confidence levels. A greedy algorithm is also presented in order to integrate data selection and large itemset generation to enhance the efficiency of the AR mining process. The experiments conducted show that XAR-Miner is more efficient in performing a large number of AR mining tasks from XML documents than the state-of-the-art method of repetitively scanning through XML documents in order to perform each of the mining tasks. Copyright © 2006, Idea Group Inc.
Source Title: Journal of Database Management
URI: http://scholarbank.nus.edu.sg/handle/10635/39709
ISSN: 10638016
Appears in Collections:Staff Publications

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