McClean, SI, Scotney, BW, Morrow, PJ and Greer, KRC (2005) Knowledge discovery by probabilistic clustering of distributed databases. Data and Knowledge Engineering, 54 (2). pp. 189-210. [Journal article]
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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.
|Item Type:||Journal article|
|Keywords:||Distributed databases; Probabilistic clustering; Aggregates; Dynamic shared ontology|
|Faculties and Schools:||Faculty of Computing & Engineering|
Faculty of Computing & Engineering > School of Computing and Information Engineering
|Research Institutes and Groups:||Computer Science Research Institute|
Computer Science Research Institute > Information and Communication Engineering
|Deposited By:||Professor Bryan Scotney|
|Deposited On:||20 Jan 2010 15:54|
|Last Modified:||15 Jun 2011 10:07|
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