Measuring structural similarity of semistructured data based on information-theoretic approaches

Measuring structural similarity of semistructured data based on information-theoretic approaches We propose and experimentally evaluate different approaches for measuring the structural similarity of semistructured documents based on information-theoretic concepts. Common to all approaches is a two-step procedure: first, we extract and linearize the structural information from documents, and then, we use similarity measures that are based on, respectively, Kolmogorov complexity and Shannon entropy to determine the distance between the documents. Compared to other approaches, we are able to achieve a linear run-time complexity and demonstrate in an experimental evaluation that the results of our technique in terms of clustering quality are on a par with or even better than those of other, slower approaches. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The VLDB Journal Springer Journals

Measuring structural similarity of semistructured data based on information-theoretic approaches

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Publisher
Springer-Verlag
Copyright
Copyright © 2012 by Springer-Verlag
Subject
Computer Science; Database Management
ISSN
1066-8888
eISSN
0949-877X
D.O.I.
10.1007/s00778-012-0263-0
Publisher site
See Article on Publisher Site

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