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

  • Conference paper
Advanced Methods for Computational Collective Intelligence

Part of the book series: Studies in Computational Intelligence ((SCI,volume 457))

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Abstract

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.

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Correspondence to Arkadiusz Lewicki .

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Lewicki, A., Pancerz, K., Tadeusiewicz, R. (2013). Ant Colony Inspired Clustering Based on the Distribution Function of the Similarity of Attributes. In: Nguyen, N., Trawiński, B., Katarzyniak, R., Jo, GS. (eds) Advanced Methods for Computational Collective Intelligence. Studies in Computational Intelligence, vol 457. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34300-1_14

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  • DOI: https://doi.org/10.1007/978-3-642-34300-1_14

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-34299-8

  • Online ISBN: 978-3-642-34300-1

  • eBook Packages: EngineeringEngineering (R0)

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