Fast discovery of association rules
Advances in knowledge discovery and data mining
Integrating association rule mining with relational database systems: alternatives and implications
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Machine Learning
RainForest - A Framework for Fast Decision Tree Construction of Large Datasets
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Integration of Data Mining with Database Technology
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
A New SQL-like Operator for Mining Association Rules
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Efficient online mining of large databases
International Journal of Business Information Systems
Software—Practice & Experience
Bitmap index-based decision trees
ISMIS'05 Proceedings of the 15th international conference on Foundations of Intelligent Systems
Shaping SQL-Based frequent pattern mining algorithms
KDID'05 Proceedings of the 4th international conference on Knowledge Discovery in Inductive Databases
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Data mining is a useful decision support technique that can be used to discover production rules in warehouses or corporate data. Data mining research has made much effort to apply various mining algorithms efficiently on large databases. However, a serious problem in their practical application is the long processing time of such algorithms. Nowadays, one of the key challenges is to integrate data mining methods within the framework of traditional database systems. Indeed, such implementations can take advantage of the efficiency provided by SQL engines.In this paper, we propose an integrating approach for decision trees within a classical database system. In other words, we try to discover knowledge from relational databases, in the form of production rules, via a procedure embedding SQL queries. The obtained decision tree is defined by successive, related relational views. Each view corresponds to a given population in the underlying decision tree. We selected the classical Induction Decision Tree (ID3) algorithm to build the decision tree. To prove that our implementation of ID3 works properly, we successfully compared the output of our procedure with the output of an existing and validated data mining software, SIPINA. Furthermore, since our approach is tuneable, it can be generalized to any other similar decision tree-based method.