Obtaining Classification Rules Using lvqPSO

  • Laura Lanzarini
  • Augusto Villa Monte
  • Germán Aquino
  • Armando De Giusti
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9140)


Technological advances nowadays have made it possible for processes to handle large volumes of historic information whose manual processing would be a complex task. Data mining, one of the most significant stages in the knowledge discovery and data mining (KDD) process, has a set of techniques capable of modeling and summarizing these historical data, making it easier to understand them and helping the decision making process in future situations. This article presents a new data mining adaptive technique called lvqPSO that can build, from the available information, a reduced set of simple classification rules from which the most significant relations between the features recorded can be derived. These rules operate both on numeric and nominal attributes, and they are built by combining a variation of a population metaheuristic and a competitive neural network. The method proposed was compared with several methods proposed by other authors and measured over 15 databases, and satisfactory results were obtained.


Classification rules Data mining Adaptive strategies Particle swarm optimization Learning vector quantization 


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Laura Lanzarini
    • 1
  • Augusto Villa Monte
    • 1
  • Germán Aquino
    • 1
  • Armando De Giusti
    • 1
  1. 1.Institute of Research in Computer Science LIDI (III-LIDI), Faculty of Computer ScienceNational University of La Plata (UNLP)La PlataArgentina

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