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      • Computing and Mathematical Sciences
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      Clustering Relational Data Based on Randomized Propositionalization

      Anderson, Grant; Pfahringer, Bernhard
      DOI
       10.1007/978-3-540-78469-2_8
      Link
       www.springerlink.com
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      Citation
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      Anderson, G. & Pfahringer, B. (2008) Clustering Relational Data Based on Randomized Propositionalization. In Proceedings of 17th International Conference, ILP 2007, Corvallis, OR, USA, June 19-21, 2007(pp. 39-48). Berlin: Springer
      Permanent Research Commons link: https://hdl.handle.net/10289/1726
      Abstract
      Clustering of relational data has so far received a lot less attention than classification of such data. In this paper we investigate a simple approach based on randomized propositionalization, which allows for applying standard clustering algorithms like KMeans to multi-relational data. We describe how random rules are generated and then turned into Boolean-valued features. Clustering generally is not straightforward to evaluate, but preliminary experimental results on a number of standard ILP datasets show promising results. Clusters generated without class information usually agree well with the true class labels of cluster members, i.e. class distributions inside clusters generally differ significantly from the global class distributions. The two-tiered algorithm described shows good scalability due to the randomized nature of the first step and the availability of efficient propositional clustering algorithms for the second step.
      Date
      2008
      Type
      Conference Contribution
      Publisher
      Springer, Berlin
      Collections
      • Computing and Mathematical Sciences Papers [1454]
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