An hybridization of an ant-based clustering algorithm with growing neural gas networks for classification tasks

ABSTRACT

Conventional ant-based clustering algorithms and growing neural gas networks are combined to produce an unsupervised classification algorithm that exploits the strengths of both techiques. The ant-based clustering algorithm detects existing classes on a training data set, and at the same time, trains several growing neural gas networks. On a second stage, these networks are used to classify previously unseen input vectors into the classes detected by the ant-based algorithm. The proposed algorithm eliminates the need of changing the number of agents and the dimensions of the environment when dealing with large databases.

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INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.5 PATTERN RECOGNITION
      I.5.3 Clustering
          Subjects: Algorithms

Additional Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.6 Learning
          Subjects: Connectionism and neural nets

Keywords:
ant-based algorithms, classification