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Data mining from multiple heterogeneous relational databases using decision tree classification
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DOI：10.1016/j.patrec.2012.05.014.png)
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Abstract
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Nowadays, the expansion of computer networks and the diversity of data sources require new data mining approaches in multi-database systems. We propose a classification approach across multiple heterogeneous relational databases. More specifically, given a set of inter-related databases, we use a regression model for predicting the most useful links that will be connected to build a multi-relational decision tree. Experiments performed on different real and synthetic databases were very satisfactory compared with previous classification approaches in multiple databases. (c) 2012 Elsevier B.V. All rights reserved.
Keywords:
Heterogeneous relational databases
Multi-database mining
Multi-relational classification
Inter-database links
Link usefulness
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