Multiobjective Approaches in Pattern Mining

  • Sebastián Ventura
  • José María Luna


In pattern mining, solutions tend to be evaluated according to several conflicting quality measures and, sometimes, these measures have to be optimized simultaneously. The problem of optimizing more than one objective function is known as multiobjective optimization, which together with evolutionary computation have given rise to evolutionary multiobjective optimization. This issue is well addressed in this chapter, which describes the usefulness of evolutionary multiobjective optimization in the pattern mining field. First, some fundamental concepts of evolutionary multiobjective optimization are introduced, including metrics to evaluate the quality of the solutions, and measures to be combined for the pattern mining problem. Then, different multiobjective approaches in the pattern mining field are described, which are divided into genetic algorithms, genetic programming approaches and other kind of algorithms like evolutionary computation and swarm intelligence. Finally, some useful real-world applications are analysed.


Pattern Mining Field Multiobjective Approach Pareto-optimal Front Multiobjective Evolutionary Algorithms (MOEAs) Single Quality Measure 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Sebastián Ventura
    • 1
  • José María Luna
    • 1
  1. 1.Department of Computer Science and Numerical AnalysisUniversity of CordobaCordobaSpain

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