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Solving Large Clustering Problems with Meta-Heuristic Search

Publikation: KonferencebidragPaperForskningpeer review

  • CORAL - Centre for Operations Research Applications in Logistics
  • Erhvervsøkonomisk Institut
In Clustering Problems, groups of similar subjects are to be retrieved from data sets. In this paper, Clustering Problems with the frequently used Minimum Sum-of-Squares Criterion are solved using meta-heuristic search. Tabu search has proved to be a successful methodology for solving optimization problems, but applications to large clustering problems are rare. The simulated annealing heuristic has mainly been applied to relatively small instances. In this paper, we implement tabu search and simulated annealing approaches and compare them to the commonly used k-means approach. We find that the meta-heuristic search methods are able to return solutions of very high quality.
StatusUdgivet - 2009
BegivenhedMIC 2009 - VIII Metaheuristic International Conference - Hamburg, Tyskland
Varighed: 13 jul. 200916 jul. 2009


KonferenceMIC 2009 - VIII Metaheuristic International Conference

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