An effective hash-based algorithm for mining association rules
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Mining quantitative association rules in large relational tables
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Efficient Mining of Association Rules in Distributed Databases
IEEE Transactions on Knowledge and Data Engineering
Efficient Similarity Search In Sequence Databases
FODO '93 Proceedings of the 4th International Conference on Foundations of Data Organization and Algorithms
ICDE '97 Proceedings of the Thirteenth 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
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Discovery of Multiple-Level Association Rules from Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
An Efficient Algorithm for Mining Association Rules in Large Databases
VLDB '95 Proceedings of the 21th 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
Effect of Data Skewness in Parallel Mining of Association Rules
PAKDD '98 Proceedings of the Second Pacific-Asia Conference on Research and Development in Knowledge Discovery and Data Mining
A Fast Algorithm for Density-Based Clustering in Large Database
PAKDD '99 Proceedings of the Third Pacific-Asia Conference on Methodologies for Knowledge Discovery and Data Mining
Expert Systems with Applications: An International Journal
Optimal leverage association rules with numerical interval conditions
Intelligent Data Analysis
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Many algorithms have been proposed for mining of boolean association rules. However, very little work has been done in mining quantitative association rules. Although we can transform quantitative attributes into boolean attributes, this approach is not effective and is difficult to scale up for high dimensional case and also may result in many imprecise association rules. Newly designed algorithms for quantitative association rules still are persecuted by nonscalable and noise problem. In this paper, an efficient algorithm, QAR-miner, is proposed. By using the notion of "density" to capture the characteristics of quantitative attributes and an efficient procedure to locate the "dense regions", QAR-miner not only can solve the problems of previous approaches, but also can scale up well for high dimensional case. Evaluations on QAR-miner have been performed using both synthetic and real databases. Preliminary results show that QAR-miner is effective and can scale up quite linearly with the increasing number of attributes.