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OPAL: A new algorithm for optimal partitioning and learning in non parametric unsupervised environments

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Abstract

A nonlinear programming approach to the problem of learning in nonparametric and unsupervised environments is put forth in this study. The approach exploits the concept of optimal partitioning of the given data set as the objective of such nonparametric unsupervised learning. This partitioning or clustering is achieved by the application of the recently developed improved flexible polyhedron method (IFPM) to the associated optimization problem. The optimization problem is defined here in terms of a new, conceptually innovative optimality criterion based on intergroup and intragroup distinct scatters. Details of this new approach, a procedure for deriving an initial partition, and application of the algorithm to two numerical examples are presented.

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Authors and Affiliations

  1. ISRO Satellite Center, Bangalore, India

    Belur V. Sheela

  2. M & S Computing, Inc., Huntsville, Alabama

    Belur V. Dasarathy

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  1. Belur V. Sheela
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  2. Belur V. Dasarathy
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Sheela, B.V., Dasarathy, B.V. OPAL: A new algorithm for optimal partitioning and learning in non parametric unsupervised environments. International Journal of Computer and Information Sciences 8, 239–253 (1979). https://doi.org/10.1007/BF00977790

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  • DOI: https://doi.org/10.1007/BF00977790

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