Introduction to artificial neural systems
Introduction to artificial neural systems
Self-Organizing Maps
Knowledge Discovery in Databases
Knowledge Discovery in Databases
Data Mining: An Overview from a Database Perspective
IEEE Transactions on Knowledge and Data Engineering
Effective Data Mining Using Neural Networks
IEEE Transactions on Knowledge and Data Engineering
Machine Learning
Database Mining: A Performance Perspective
IEEE Transactions on Knowledge and Data Engineering
An Attribute-Oriented Approach for Learning Classification Rules from Relational Databases
Proceedings of the Sixth International Conference on Data Engineering
An Interval Classifier for Database Mining Applications
VLDB '92 Proceedings of the 18th International Conference on Very Large Data Bases
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Data mining is about extracting hidden information from a large data set. One task of data mining is to describe the characteristics of the data set using attributes in the form of rules. This paper aims to develop a neural networks based framework for the fast mining of characteristic rules. The idea is to first use the Kohonen map to cluster the data set into groups with common similar features. Then use a set of single-layer supervised neural networks to model each of the groups so that the significant attributes characterizing the data set can be extracted. An incremental algorithm combining these two steps is proposed to derive the characteristic rules for the data set with nonlinear relations. The framework is tested using a large size problem of forensic data of heart patients. Its effectiveness is demonstrated.