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Scale-based clustering using the radial basis function network.

S V Chakravarthy1, J Ghosh

  • 1Dept. of Electr. and Comput. Eng., Texas Univ., Austin, TX.

IEEE Transactions on Neural Networks
|January 1, 1996
PubMed
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This study introduces a scale-based clustering method using radial basis function networks (RBFN). The RBF width parameter determines the optimal clustering scale, improving RBFN performance and identifying key properties for clustering basis functions.

Area of Science:

  • Computational Vision
  • Machine Learning
  • Data Mining

Background:

  • Determining the optimal scale for data clustering is a significant challenge.
  • Radial Basis Function Networks (RBFN) are powerful tools for data analysis but require careful parameter selection.
  • Existing clustering methods often struggle with identifying the appropriate number of clusters and network parameters.

Purpose of the Study:

  • To propose a novel scale-based clustering technique utilizing Radial Basis Function Networks (RBFN).
  • To demonstrate how the RBF width parameter can serve as a scale parameter for effective clustering.
  • To provide a method for determining the optimal scale, number of RBF units, and widths for robust network solutions.

Main Methods:

  • Implementing a scale-based clustering approach within the RBFN framework.

Related Experiment Videos

  • Utilizing the RBF width as the primary scale parameter.
  • Employing a dummy target output to guide the network's learning process.

    • Main Results:

    • The proposed RBFN-based method successfully identifies the optimal clustering scale for datasets.
    • The technique effectively determines the appropriate number of RBF units and their corresponding widths.
    • Performance evaluation on benchmark datasets shows favorable comparisons with standard clustering techniques.

    Conclusions:

    • The RBF width parameter plays a crucial role in RBFN for scale-based clustering.
    • This approach offers a fundamental link between RBFN and scale-space theory in computational vision.
    • The method provides a robust solution for parameter selection in RBFN clustering.