An algorithm for minimizing clustering functions

The problem of cluster analysis is formulated as a problem of nonsmooth, nonconvex optimization. An algorithm for solving the latter optimization problem is developed which allows one to significantly reduce the computational efforts. This algorithm is based on the so-called discrete gradient method...

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Bibliographic Details
Published inOptimization Vol. 54; no. 4-5; pp. 351 - 368
Main Authors Bagirov, Adil M., Ugon, Julien
Format Journal Article
LanguageEnglish
Published Philadelphia Taylor & Francis Group 01.08.2005
Taylor & Francis LLC
Subjects
2000 Mathematics Subject Classifications: 49M25
Algorithms
Cluster analysis
Clustering functions
Discrete gradient method
Nonsmooth, nonconvex optimization
Optimization algorithms
Studies
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ISSN0233-1934
1029-4945
DOI10.1080/02331930500096155

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Summary:The problem of cluster analysis is formulated as a problem of nonsmooth, nonconvex optimization. An algorithm for solving the latter optimization problem is developed which allows one to significantly reduce the computational efforts. This algorithm is based on the so-called discrete gradient method. Results of numerical experiments are presented which demonstrate the effectiveness of the proposed algorithm.
Bibliography:SourceType-Scholarly Journals-1
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ISSN:0233-1934
1029-4945
DOI:10.1080/02331930500096155