Semantic Approach to Cluster Validity Notion

  • Elena Sivogolovko
  • Bernhard Thalheim
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 186)


In our research we formulate new concepts of the cluster quality based on semantic point of view. In the presented cluster validity approaches quality of clustering is measured according to correspondence between dataset and cluster structure or some cluster structure properties. Cluster semantic and user interests are not considered. We present a semantic approach to cluster validity and a methodology of its evaluation.


Cluster Model Semantic Similarity Cluster Structure Resource Description Framework Semantic Quality 
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.
  2. 2.
    Iso standard 9000-2000: Quality management systems: Fundamentals and vocabulary (2000)Google Scholar
  3. 3.
    Albagli, S., Ben-Eliyahu-Zohary, R., Shimony, S.E.: Markov network based ontology matching. In: Proc. The 21st International Joint Conference on Artifical Intelligence IJCAI (2009)Google Scholar
  4. 4.
    Berry, M., Linoff, G.: Data Mining Techniques For Marketing, Sales and Customer Support. John Wiley & Sons, Inc. (1996)Google Scholar
  5. 5.
    Doshi, P., Kolli, R., Thomas, C.: Inexact matching of ontology graphs using expectation-maximization. Journal Web Semantics: Science, Services and Agents on the World Wide Web 7(2) (2009)Google Scholar
  6. 6.
    Dunn, J.: Well separated clusters and optimal fuzzy-partitions. Journal of Cybernetics 4, 95–104 (1974)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Halkidi, M., Batistakis, Y., Vazirgiannis, M.: On clustering validation techniques. Intelligent Information Systems Journal 17, 107–145 (2001)zbMATHCrossRefGoogle Scholar
  8. 8.
    Hu, W., Jian, N., Qu, Y., Wang, Y.: Gmo: A graph matching for ontologies. In: K-Cap 2005 Workshop on Integrating Ontologies (2005)Google Scholar
  9. 9.
    Jain, A.K., Dubes, R.C.: Algorithms for Clustering Data. Prentice-Hall (1988)Google Scholar
  10. 10.
    Kidawara, Y., Zettsu, K., Kiyoki, Y., Jannaschk, K., Thalheim, B., Linna, P., Jaakkola, H., Duzí, M.: Knowledge modeling, management and utilization towards next generation web. In: Proc. of the 2010 Conference on Information Modelling and Knowledge Bases XXI (2010)Google Scholar
  11. 11.
    Lindland, O.I., Sindre, G., Solvberg, A.: Understanding quality in conceptual modelling. IEEE Software, 42–49 (1994)Google Scholar
  12. 12.
    Manola, F., Miller, E. (eds.): W3C Recommendation, chap. RDF Primer (2004),
  13. 13.
    Michalski, R.S., Stepp, R.E.: Learning from observation: Conceptual clustering. In: Michalski, R.S., Carbonell, J.G., Mitchell, T.M. (eds.) Machine Learning: An Artificial Intelligence Approach, Tioga, ch.11, pp. 331–364 (1983)Google Scholar
  14. 14.
    Oldakowski, R., Bizer, C.: Semmf: A framework for calculating semantic similarity of objects represented as rdf graphs. In: Poster at the 4th International Semantic Web Conference, ISWC 2005 (2005)Google Scholar
  15. 15.
    Tous, R., Delgado, J.: A Vector Space Model for Semantic Similarity Calculation and OWL Ontology Alignment. In: Bressan, S., Küng, J., Wagner, R. (eds.) DEXA 2006. LNCS, vol. 4080, pp. 307–316. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  16. 16.
    Wanner, P. (ed.): The Cambridge Encyclopedia of Language. Cambridge University Press (1987)Google Scholar
  17. 17.
    Zhang, R., Wang, Y., Wang, J.: Research on ontology matching approach in semantic web. In: Proc. International Conference on Internet Computing in Science and Engineering ICICSE 2008, pp. 254–257 (2008)Google Scholar

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© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Saint-Petersburg State UniversityPetersburgRussia
  2. 2.Institut für Informatik der Christian-Albrechts-Universität zu KielKielGermany

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