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
Automatic subspace clustering of high dimensional data for data mining applications
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
OPTICS: ordering points to identify the clustering structure
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Towards the building of a dense-region-based OLAP system
Data & Knowledge Engineering
Data Mining with optimized two-dimensional association rules
ACM Transactions on Database Systems (TODS)
Discovering associations with numeric variables
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient Mining of Association Rules in Distributed Databases
IEEE Transactions on Knowledge and Data Engineering
A Statistical Theory for Quantitative Association Rules
Journal of Intelligent Information Systems
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
Efficient and Effective Clustering Methods for Spatial Data Mining
VLDB '94 Proceedings of the 20th 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
SPRINT: A Scalable Parallel Classifier for Data Mining
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
STING: A Statistical Information Grid Approach to Spatial Data Mining
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
DBRS: a density-based spatial clustering method with random sampling
PAKDD'03 Proceedings of the 7th Pacific-Asia conference on Advances in knowledge discovery and data mining
MRM: A matrix representation and mapping approach for knowledge acquisition
Knowledge-Based Systems
Mining fuzzy association rules from questionnaire data
Knowledge-Based Systems
Rough-set-based association rules applied to brand trust evaluation model
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: theory and algorithms - Volume Part I
Mining fuzzy specific rare itemsets for education data
Knowledge-Based Systems
Multivariate discretization for associative classification in a sparse data application domain
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part I
The rough set-based algorithm for two steps
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
Relative association rules based on rough set theory
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
Information Sciences: an International Journal
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Many algorithms have been proposed for mining 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 cases and also may result in many imprecise association rules. Newly designed algorithms for quantitative association rules still are persecuted by the problems of nonscalability and noise. In this paper, an efficient algorithm, DRMiner, is proposed. By using the notion of ''density'' to capture the characteristics of quantitative attributes and an efficient procedure to locate the ''dense regions'', DRMiner not only can solve the problems of previous approaches, but also can scale up well for high-dimensional cases. Evaluations on DRMiner have been performed using synthetic databases. The results show that DRMiner is effective and can scale up quite linearly with the increasing number of attributes.