Neural networks: algorithms, applications, and programming techniques
Neural networks: algorithms, applications, and programming techniques
Multi-relational Decision Tree Induction
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
Identifying Relevant Databases for Multidatabase Mining
PAKDD '98 Proceedings of the Second Pacific-Asia Conference on Research and Development in Knowledge Discovery and Data Mining
A survey of approaches to automatic schema matching
The VLDB Journal — The International Journal on Very Large Data Bases
Database classification for multi-database mining
Information Systems
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Efficient Classification across Multiple Database Relations: A CrossMine Approach
IEEE Transactions on Knowledge and Data Engineering
An Efficient Relational Decision Tree Classification Algorithm
ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 03
Scalable mining and link analysis across multiple database relations
Scalable mining and link analysis across multiple database relations
Mining globally interesting patterns from multiple databases using kernel estimation
Expert Systems with Applications: An International Journal
An Improved Database Classification Algorithm for Multi-database Mining
FAW '09 Proceedings of the 3d International Workshop on Frontiers in Algorithmics
Developing Multi-Database Mining Applications
Developing Multi-Database Mining Applications
Efficient classification from multiple heterogeneous databases
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
Knowledge Discovery in Multiple Databases
Knowledge Discovery in Multiple Databases
Clustering local frequency items in multiple databases
Information Sciences: an International Journal
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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.