Building Classes in Object-Based Languages by Automatic Clustering

  • Petko Valtchev
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1642)


The paper deals with clustering of objects described both by properties and relations. Relational attributes may make object descriptions recursively depend on themselves so that attribute values cannot be compared before objects themselves are. An approach to clustering is presented whose core element is an object dissimilarity measure. All sorts of object attributes are compared in a uniform manner with possible exploration of the existing taxonomic knowledge. Dissimilarity values for mutually dependent object couples are computed as solutions of a system of linear equations. An example of building classes on objects with self-references demonstrates the advantages of the suggested approach.


Object Attribute Conceptual Cluster Abstract Data Type Single Linkage Cluster Automatic Cluster 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. [1]
    H. W. Beck, T. Anwar, and S. B. Navathe. A conceptual clustering algorithm for database schema design. IEEE Transactions on Knowledge and Data Engineering, pages 396–411, 1994.Google Scholar
  2. [2]
    G. Bisson. Conceptual clustering in a first order logic representation. In Proceedings of the 10th European Conference on Artificial Intelligence, Vienna, Austria, pages 458–462, 1992.Google Scholar
  3. [3]
    G. Bisson. Why and how to define a similarity measure for object-based representation systems. In N. J. I. Mars,editor, Towards Very Large Knowledge Bases, pages 236–246, Amsterdam, 1995. IOS Press.Google Scholar
  4. [4]
    G. Booch. Object-oriented analysis and design with applications. BenjaminCummings, 1994.Google Scholar
  5. [5]
    J. Euzenat. Brief overview of t-tree: the tropes taxonomy building tool. In Proceedings of the 4th ASIS SIG/CR Classification research workshop, Columbus (OH US), pages 69–87, 1993.Google Scholar
  6. [6]
    D. H. Fisher. Knowledge acquisition via incremental conceptual clustering. Machine Learning, 2:139–172, 1987.Google Scholar
  7. [7]
    J. Han, S. Nishio, H. Kawano, and W. Wang. Generalization-based data mining in object-oriented databases using an object-cube model. IEEE Transactions on Knowlwdge and Data Engineering, 25(1):55–97, 1998.zbMATHCrossRefGoogle Scholar
  8. [8]
    INRIA Rhüne-Alpes, Grenoble (FR). Tropes 1.0 reference manual, 1995.Google Scholar
  9. [9]
    J.-U. Kietz and K. Morik. A polynomial approach to the constructive induction of structural knowledge. Machine Learning, 14(2):193–217, 1994.zbMATHCrossRefGoogle Scholar
  10. [10]
    R. Michalski and R. Stepp. Machine learning: an Artificial Intelligence approach, volume I, chapter Learning from observation: conceptual clustering, pages 331–363. Tioga publishing company, Palo Alto (CA US), 1983.Google Scholar
  11. [11]
    P. Valtchev and J. Euzenat. Dissimilarity measure for collections of objects and values. In P. Coen X. Liu and M. Berthold,editors, Proceedings of the 2nd Symposium on Intelligent Data Analysis., volume 1280 of Lecture Notes in Computer Science, pages 259–272, 1997.Google Scholar
  12. [12]
    B. van Cutsem. Classification and dissimilarity analysis, volume 93 of Lecture notes in statistics. Springer Verlag, New York, 1994.zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1999

Authors and Affiliations

  • Petko Valtchev
    • 1
  1. 1.INRIA Rhône-AlpesMontbonnot Saint-MartinFrance

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