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Data on cut-edge for spatial clustering based on proximity graphs.

Alper Aksac1, Tansel Ozyer2, Reda Alhajj1,3

  • 1Department of Computer Science, University of Calgary, Calgary, AB, Canada.

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Summary
This summary is machine-generated.

This study introduces CutESC-P, a parametric spatial clustering algorithm. It refines the CutESC method by detailing optimal parameters and providing supporting data for enhanced knowledge discovery in spatial data mining.

Keywords:
ClusteringGraph theoryProximity graphsSpatial data mining

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Area of Science:

  • Spatial data mining
  • Knowledge discovery
  • Computational geometry

Background:

  • Cluster analysis is crucial for automating knowledge discovery in spatial data mining.
  • Effective clustering requires high intra-cluster similarity and low inter-cluster similarity.
  • The CutESC (Cut-Edge for Spatial Clustering) algorithm, a graph-based approach, was previously introduced.

Purpose of the Study:

  • To present the parametric version of the CutESC algorithm, named CutESC-P.
  • To share optimal parameter settings for the CutESC algorithm based on experimental analysis.
  • To provide supplementary data and analyses supporting the original CutESC research.

Main Methods:

  • Development of a parametric version (CutESC-P) of the CutESC spatial clustering algorithm.
  • Graph-based approach for spatial data clustering.
  • Identification and validation of optimal parameter settings through experimentation.

Main Results:

  • The CutESC-P algorithm offers a refined approach to spatial clustering.
  • Specific parameter settings are identified as optimal for experimental conditions.
  • Additional analyses demonstrate the robustness and utility of the CutESC algorithm.

Conclusions:

  • The parametric CutESC-P algorithm enhances the applicability of spatial clustering.
  • Optimal parameter selection is key to maximizing clustering performance.
  • The provided data and analyses support the broader adoption of CutESC for spatial data mining tasks.