Knowledge discovery by probabilistic clustering of distributed databases

SI McClean, BW Scotney, PJ Morrow, KRC Greer

Research output: Contribution to journalArticlepeer-review

18 Citations (Scopus)


Clustering of distributed databases facilitates knowledge discovery through learning of new concepts that characterise common features and differences between datasets. Hence, general patterns can be learned rather than restricting learning to specific databases from which rules may not be generalisable. We cluster databases that hold aggregate count data on categorical attributes that have been classified according to homogeneous or heterogeneous classification schemes. Clustering of datasets is carried out via the probability distributions that describe their respective aggregates. The homogeneous case is straightforward. For heterogeneous data we investigate a number of clustering strategies, of which the most efficient avoid the need to compute a dynamic shared ontology to homogenise the classification schemes prior to clustering.
Original languageEnglish
Pages (from-to)189-210
JournalData and Knowledge Engineering
Issue number2
Publication statusPublished (in print/issue) - Aug 2005


  • Distributed databases
  • Probabilistic clustering
  • Aggregates
  • Dynamic shared ontology


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