Semigroup Forum

, Volume 82, Issue 2, pp 242–251 | Cite as

Optimization of classifiers for data mining based on combinatorial semigroups



The aim of the present article is to obtain a theoretical result essential for applications of combinatorial semigroups for the design of multiple classification systems in data mining. We consider a novel construction of multiple classification systems, or classifiers, combining several binary classifiers. The construction is based on combinatorial Rees matrix semigroups without any restrictions on the sandwich-matrix. Our main theorem gives a complete description of all optimal classifiers in this novel construction.


Combinatorial semigroups Data mining 


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Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • A. V. Kelarev
    • 1
  • J. L. Yearwood
    • 1
  • P. A. Watters
    • 1
  1. 1.School of Science, Information Technology and EngineeringUniversity of BallaratBallaratAustralia

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