Abstract
Distributed databases offer much as a potentially fruitful area for Knowledge Discovery since they allow us to combine data from different, heterogeneous, sources which have not previously been integrated. We provide a methodology for combining continuous or ordinal data that classifies by grouping contiguous values into the same class. Such data is obtained from data which have been horizontally fragmented within the distributed database management system, and which may be held at different levels of granularity. Integration is accomplished by using the intersection hypergraph to produce the integrated universal classification scheme and to determine the cardinalities of each class within the universal table.
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McClean, S.I., Scotney, B.W. (2000). Uncertainty Handling for Distributed Database Integration and Knowledge Discovery. In: Bouchon-Meunier, B., Yager, R.R., Zadeh, L.A. (eds) Information, Uncertainty and Fusion. The Springer International Series in Engineering and Computer Science, vol 516. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-5209-3_16
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DOI: https://doi.org/10.1007/978-1-4615-5209-3_16
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