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An Overview of Artificial Life Approaches for Clustering

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From Data and Information Analysis to Knowledge Engineering

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Abstract

Recently, artificial life approaches for clustering have been proposed. However, the research on artificial life is mainly the simulation of systems based on models for real life. In addition to that artificial life methods have been utilized to solve optimization problems. This paper gives a short overview of artificial life and its applications in general. From this starting point we will focus on artificial life approaches used for clustering. These approaches are characterized by the fact that solutions are emergent rather than predefined and preprogrammed. The data is seen as active rather than passive objects. New data can be added incrementally to the system. We will present existing concepts for clustering with artificial life and highlight their differences and strengths.

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Author information

Authors and Affiliations

  1. Databionics Research Group, Institute for Mathematics and Computer Science, Philipps Universität Marburg, 35032, Marburg/Lahn, Germany

    David Kämpf & Alfred Ultsch

Authors
  1. David Kämpf
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  2. Alfred Ultsch
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Editors and Affiliations

  1. Institut für Technische und Betriebliche Informationssysteme, Otto-von-Guericke-Universität Magdeburg, Universitätsplatz 2, 39106, Magdeburg, Germany

    Myra Spiliopoulou

  2. Institut für Wissens- und Sprachverarbeitung, Otto-von-Guericke-Universität Magdeburg, Universitätsplatz 2, 39106, Magdeburg, Germany

    Rudolf Kruse , Christian Borgelt  & Andreas Nürnberger ,  & 

  3. Institut für Entscheidungstheorie und Unternehmensforschung, Universität Karlsruhe (TH), 76128, Karlsruhe

    Wolfgang Gaul

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Kämpf, D., Ultsch, A. (2006). An Overview of Artificial Life Approaches for Clustering. In: Spiliopoulou, M., Kruse, R., Borgelt, C., Nürnberger, A., Gaul, W. (eds) From Data and Information Analysis to Knowledge Engineering. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-31314-1_59

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