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Ant Clustering Algorithm with Information Theoretic Learning

  • Urszula BoryczkaEmail author
  • Mariusz Boryczka
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 56)

Abstract

In this paper a novel ant clustering algorithm with information theoretic learning consolidation is presented. Derivation of the information potential and its force from Renyi’s entropy have been used to create an interesting model of ant’s movement during clusterization process. In this approach each object is treated as a single agent-ant. What is more, in a local environment each agent-ant moves in accordance to information forces influence. The outcome of all information forces determines the direction and range of agent-ants’ movement. Stopping criterion used in this approach indirectly emerges from Renyi entropy. This modified algorithm has been tested on different data sets and comparative study shows the effectiveness of the proposed clustering algorithm.

Keywords

Ant clustering algorithm Data mining Information theoretic learning 

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Institute of Computer Science, University of SilesiaSosnowiecPoland

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