An Effective Solution to Adequate and Operative Duplicate Detection in Stratified Data
Journal Title: IOSR Journals (IOSR Journal of Computer Engineering) - Year 2014, Vol 16, Issue 2
Abstract
Abstract: Data Mining is considered as a nontrival extraction of implicit, previously unknown and potentially usefull information from data. Although there is a long line of work on identifying duplicates in relational data, only a few solutions focus on duplicate detection in more complex hierarchical structures, like XML data. A novel method for XML duplicate detection, called XMLDup. XMLDup uses a Bayesian network to determine the probability of two XML elements being duplicates, considering not only the information within the elements, but also the way that information is structured. In addition, to improve the efficiency of the network evaluation a novel pruning strategy, capable of significant gains of the unoptimized version of the algorithm, is presented. Through experiments Bayesian Network proves that it can achieve high precision and recall scores in several data sets. XMLDup is also able to outperform another state-of-the-art duplicate detection solution, both in terms of upto 80% and of effectiveness.
Authors and Affiliations
A. Baladhandayutham, , S. Roselin Mary
Keywords
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- EP ID EP142076
- DOI 10.9790/0661-1628101105
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