Data mining from multiple heterogeneous relational databases using decision tree classification

Abstract

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.