NDLI: Duplicate detection through structure optimization

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Suggestion set utility maximization using session logs

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Scalable density-based subspace clustering

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Duplicate detection through structure optimization

Content Provider	ACM Digital Library
Author	Calado, Pável Leitão, Luís
Abstract	Detecting and eliminating duplicates in databases is a task of critical importance in many applications. Although solutions for traditional models, such as relational data, have been widely studied, recently there has been some focus on solutions for more complex hierarchical structures as, for instance, XML data. Such data presents many different challenges, among which is the issue of how to exploit the schema structure to determine if two objects are duplicates. In this paper, we argue that structure can indeed have a significant impact on the process of duplicate detection. We propose a novel method that automatically restructures database objects in order to take full advantage of the relations between its attributes. This new structure reflects the relative importance of the attributes in the database and avoids the need to perform a manual selection. To test our approach we applied it to an existing duplicate detection system. Experiments performed on several datasets show that, using the new learned structure, we consistently outperform both the results obtained with the original database structure and those obtained by letting a knowledgeable user manually choose the attributes to compare.
Starting Page	443
Ending Page	452
Page Count	10
File Format	PDF
ISBN	9781450307178
DOI	10.1145/2063576.2063644
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2011-10-24
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Duplicate detection Hierarchical data Simulated annealing Data quality Object structure Support vector machine
Content Type	Text
Resource Type	Article

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