Ant Colony Inspired Clustering Based on the Distribution Function of the Similarity of Attributes

  • Arkadiusz LewickiEmail author
  • Krzysztof Pancerz
  • Ryszard Tadeusiewicz
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 457)


The paper presents results of research on the clustering problem on the basis of swarm intelligence using a new algorithm based on the normalized cumulative distribution function of attributes. In this approach, we assume that the analysis of likelihood of the occurrence of particular types of attributes and their values allows us to measure the similarity of the objects within a given category and the dissimilarity of the objects between categories. Therefore, on the basis of the complex data set of attributes of any type, we can successfully raise a lot of interesting information about these attributes without necessity of considering their real meaning. Our research shows that the algorithm inspired by the mechanisms observed in nature may return better results due to the modification of the neighborhood based on the similarity coefficient.


ant colony clustering analysis ant colony optimization swarm intelligence self-organization unsupervised clustering data mining distribution function 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Arkadiusz Lewicki
    • 1
    Email author
  • Krzysztof Pancerz
    • 1
  • Ryszard Tadeusiewicz
    • 2
  1. 1.University of Information Technology and Management in RzeszówRzeszówPoland
  2. 2.AGH University of Science and TechnologyKrakówPoland

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