Not logged in.
Quick Search - Contribution
Contribution Details
| Type | Journal Article |
| Scope | Discipline-based scholarship |
| Title | Measuring structural similarity of semistructured data based on information-theoretic approaches |
| Organization Unit | |
| Authors |
|
| Item Subtype | Original Work |
| Refereed | Yes |
| Status | Published in final form |
| Language |
|
| Journal Title | VLDB Journal |
| Publisher | Springer |
| Geographical Reach | international |
| ISSN | 1066-8888 |
| Volume | 21 |
| Number | 5 |
| Page Range | 677 - 702 |
| Date | 2012 |
| Abstract Text | 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. |
| Digital Object Identifier | 10.1007/s00778-012-0263-0 |
| Other Identification Number | merlin-id:7762 |
| PDF File |
Download from ZORA
|
| Export |
BibTeX
EP3 XML (ZORA)
|
| Keywords | Hardware and Architecture, Information Systems |