Geometric approach to data mining

Wladimir Rodriguez, Mark Last, Abraham Kandel, Horst Bunke

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


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.

Original languageEnglish
Pages (from-to)363-386
Number of pages24
JournalInternational Journal of Image and Graphics
Issue number2
StatePublished - 1 Apr 2001
Externally publishedYes


  • Data Mining
  • Geometric Invariance
  • Shape Matching
  • String Edit Distance
  • Three-Dimensional Curve Similarity

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design


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