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A Family of Two-Dimensional Benchmark Data Sets and Its Application to Comparing Different Cluster Validation Indices

  • Jorge M. Santos
  • Mark Embrechts
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8495)

Abstract

There are two main objectives in this paper: the first one is to introduce a collection of two-dimensional benchmark data sets with a wide variety of clustering characteristics that are typical for real-world data sets. These simple 2-D data sets allow the user to easily evaluate clustering solutions from a variety of different clustering algorithms; the second one is to evaluate four different commonly used clustering validation indices by using these 2-D benchmark data sets. It is shown that even for simple 2-D data sets there is a large discrepancy on the ideal number of clusters suggested by traditional cluster validation indices. The performed experiments also suggest that the Dunn and the GAP statistic seems to be more robust cluster validation indices, even though they still fail to comply with common sense clustering solutions in more than 50% of the cases.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Jorge M. Santos
    • 1
    • 2
  • Mark Embrechts
    • 3
  1. 1.ISEP, School of EngineeringPolytechnic of Porto - Dept. of MathematicsPortugal
  2. 2.INEB, Biomedical Engineering InstitutePortoPortugal
  3. 3.Dept. Ind. Systems Eng.Rensselaer Polytechnic InstituteTroyUSA

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