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
Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
MSQL: A Query Language for Database Mining
Data Mining and Knowledge Discovery
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
Sampling Large Databases for Association Rules
VLDB '96 Proceedings of the 22th 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
Decision Tree Modeling with Relational Views
ISMIS '02 Proceedings of the 13th International Symposium on Foundations of Intelligent Systems
Data Organization and Access for Efficient Data Mining
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Efficient Integration of Data Mining Techniques in Database Management Systems
IDEAS '04 Proceedings of the International Database Engineering and Applications Symposium
ATLAS: a small but complete SQL extension for data mining and data streams
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Efficient online mining of large databases
International Journal of Business Information Systems
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In this paper we propose an original approach to apply data mining algorithms, namely decision tree-based methods, taking into account not only the size of processed databases but also the processing time. The key idea consists in constructing a decision tree, within the DBMS, using bitmap indices. Indeed bitmap indices have many useful properties such as the count and bit-wise operations. We will show that these operations are efficient to build decision trees. In addition, by using bitmap indices, we don't need to access raw data. This implies clear improvements in terms of processing time.