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Conceptual Clustering of Multi-Relational Data

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Inductive Logic Programming (ILP 2011)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7207))

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Abstract

“Traditional” clustering, in broad sense, aims at organizing objects into groups (clusters) whose members are “similar” among them and are “dissimilar” to objects belonging to the other groups. In contrast, in conceptual clustering the underlying structure of the data together with the description language which is available to the learner is what drives cluster formation, thus providing intelligible descriptions of the clusters, facilitating their interpretation.

We present a novel conceptual clustering system for multi-relational data, based on the popular k − medoids algorithm. Although clustering is, generally, not straightforward to evaluate, experimental results on several applications show promising results. Clusters generated without class information agree very well with the true class labels of cluster’s members. Moreover, it was possible to obtain intelligible and meaningful descriptions of the clusters.

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Author information

Authors and Affiliations

  1. CRACS-INESC Porto LA, Universidade do Porto, Rua do Campo Alegre 1021/1055, 4169-007, Porto, Portugal

    Nuno A. Fonseca & Vítor Santos Costa

  2. EMBL Outstation, The European Bioinformatics Institute (EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK

    Nuno A. Fonseca

  3. DCC-FCUP, Universidade do Porto, Rua do Campo Alegre 1021/1055, 4169-007, Porto, Portugal

    Vítor Santos Costa

  4. LIAAD-INESC Porto LA & DEI-FEUP, Universidade do Porto, Rua Dr. Roberto Frias s/n, 4200-465, Porto, Portugal

    Rui Camacho

Authors
  1. Nuno A. Fonseca
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  2. Vítor Santos Costa
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  3. Rui Camacho
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Editor information

Editors and Affiliations

  1. Department of Computer Science, Imperial College London, 180 Queen’s Gate, SW7 2AZ, London, UK

    Stephen H. Muggleton  & Alireza Tamaddoni-Nezhad  & 

  2. Dipartimento di Informatica, Università degli Studi di Bari “Aldo Moro”, Via E. Orabona, 4, 70125, Bari, Italy

    Francesca A. Lisi

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Fonseca, N.A., Santos Costa, V., Camacho, R. (2012). Conceptual Clustering of Multi-Relational Data. In: Muggleton, S.H., Tamaddoni-Nezhad, A., Lisi, F.A. (eds) Inductive Logic Programming. ILP 2011. Lecture Notes in Computer Science(), vol 7207. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31951-8_16

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  • DOI: https://doi.org/10.1007/978-3-642-31951-8_16

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-31950-1

  • Online ISBN: 978-3-642-31951-8

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