Abstract
A framework is proposed to build small scale models of very large databases describing several entities and their relationships. In the first part, it is shown that the use of sampling is not a good solution when several entities are stored in a database. In the second part, a model is proposed which is based on clustering all entities of the database and storing aggregates on the clusters and on the relationships between the clusters. The last part of the paper discusses the different problems which are raised by this approach. Some solutions are proposed: in particular, the link with symbolic data analysis is established.
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Hébrail, G., Lechevallier, Y. (2004). Building Small Scale Models of Multi-Entity Databases By Clustering. In: Banks, D., McMorris, F.R., Arabie, P., Gaul, W. (eds) Classification, Clustering, and Data Mining Applications. Studies in Classification, Data Analysis, and Knowledge Organisation. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17103-1_37
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DOI: https://doi.org/10.1007/978-3-642-17103-1_37
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22014-5
Online ISBN: 978-3-642-17103-1
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