Journal article
GEOMETRIC APPROACH TO DATA MINING
- RODRIGUEZ, WLADIMIR Department of Computer Sciences, Faculty of Engineering, Universidad de Los Andes, Mérida, Venezuela
- LAST, MARK Computer Science and Engineering Department, University of South Florida, 4202 East Fowler Avenue, Tampa, Fl 33620-5399, USA
- KANDEL, ABRAHAM Computer Science and Engineering Department, University of South Florida, 4202 East Fowler Avenue, Tampa, Fl 33620-5399, USA
- BUNKE, HORST Institut fur Informatik and angewandte Mathematik, University of Bern, Bern, Switzerland
- 2011-11-20
Published in:
- International Journal of Image and Graphics. - World Scientific Pub Co Pte Lt. - 2001, vol. 01, no. 02, p. 363-386
English
In this paper, a new, geometric approach to pattern identification in data mining is presented. It is based on applying string edit distance computation to measuring the similarity between multi-dimensional curves. The string edit distance computation is extended to allow the possibility of using strings, where each element is a vector rather than just a symbol. We discuss an approach for representing 3D-curves using the curvature and the tension as their symbolic representation. This transformation preserves all the information contained in the original 3D-curve. We validate this approach through experiments using synthetic and digitalized data. In particular, the proposed approach is suitable to measure the similarity of 3D-curves invariant under translation, rotation, and scaling. It also can be applied for partial curve matching.
- Language
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- English
- Open access status
- closed
- Identifiers
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- DOI 10.1142/s0219467801000220
- ISSN 0219-4678
- Persistent URL
- https://folia.unifr.ch/global/documents/41677
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