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Counting clusters using R-NN curves.

Rajarshi Guha1, Debojyoti Dutta, David J Wild

  • 1School of Informatics, Indiana University, Bloomington, Indiana 47406, USA. rguha@indiana.edu

Journal of Chemical Information and Modeling
|July 3, 2007
PubMed
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This study introduces the R-NN curve algorithm to determine the optimal number of clusters (k) for k-means clustering in cheminformatics. The R-NN curve method accurately estimates k, aligning with cluster quality measures.

Area of Science:

  • Cheminformatics
  • Computational Chemistry
  • Data Mining

Background:

  • Nonhierarchical clustering, like k-means, requires specifying the number of clusters (k).
  • Traditional methods involve iterative clustering with varying k values to find the optimum.
  • Determining the optimal k a priori is crucial for efficient and accurate clustering.

Purpose of the Study:

  • To introduce and evaluate the R-NN curve algorithm for a priori selection of k in clustering.
  • To assess the algorithm's ability to estimate the natural number of clusters.
  • To compare the R-NN curve algorithm's results with established cluster quality measures.

Main Methods:

  • Utilized the R-NN curve algorithm, based on nearest-neighbor analysis, to characterize compound spatial distributions.

Related Experiment Videos

  • Generated and analyzed R-NN curves to estimate the natural number of clusters.
  • Performed k-means clustering using the predicted k and compared results with average silhouette width.

    • Main Results:

    • The R-NN curve algorithm successfully determined the natural number of clusters for various datasets.
    • Results showed general agreement between the R-NN curve algorithm and average silhouette width in identifying optimal k.
    • The algorithm demonstrated effectiveness on both simulated and real chemical data.

    Conclusions:

    • The R-NN curve algorithm provides a reliable method for a priori determination of k in clustering.
    • This approach simplifies and enhances the efficiency of clustering in cheminformatics.
    • The R-NN curve algorithm is a valuable tool for selecting optimal cluster numbers, complementing existing quality metrics.