Handling infinite temporal data
Selected papers of the 9th annual ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
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
Data mining using two-dimensional optimized association rules: scheme, algorithms, and visualization
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Mining optimized association rules for numeric attributes
PODS '96 Proceedings of the fifteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
The analysis of relationships in databases for rule derivation
Journal of Intelligent Information Systems
Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Association rules over interval data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Fast discovery of association rules
Advances in knowledge discovery and data mining
Data-Driven Discovery of Quantitative Rules in Relational Databases
IEEE Transactions on Knowledge and Data Engineering
Mining Knowledge Rules from Databases: A Rough Set Approach
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
Maintenance of Discovered Association Rules in Large Databases: An Incremental Updating Technique
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th 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
Discovering roll-up dependencies
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Searching for dependencies at multiple abstraction levels
ACM Transactions on Database Systems (TODS)
PADKK '00 Proceedings of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Current Issues and New Applications
Optimized Disjunctive Association Rules via Sampling
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Optimization of a language for data mining
Proceedings of the 2003 ACM symposium on Applied computing
On the complexity of inducing categorical and quantitative association rules
Theoretical Computer Science
Finding the most interesting correlations in a database: how hard can it be?
Information Systems
Market basket analysis in a multiple store environment
Decision Support Systems
The complexity of non-hierarchical clustering with instance and cluster level constraints
Data Mining and Knowledge Discovery
ACM SIGKDD Explorations Newsletter
Re-mining item associations: Methodology and a case study in apparel retailing
Decision Support Systems
Quantitative and ordinal association rules mining (QAR mining)
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part I
Automated user modeling for personalized digital libraries
International Journal of Information Management: The Journal for Information Professionals
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The discovery of quantitative association rules in largedatabases is considered an interesting and important researchproblem. Recently, different aspects of the problem have beenstudied, and several algorithms have been presented in theliterature, among others in (Srikant and Agrawal, 1996; Fukuda etal., 1996a; Fukuda et al., 1996b; Yoda et al., 1997; Miller and Yang,1997). An aspect of the problem that has so far been ignored, is itscomputational complexity. In this paper, we study the computationalcomplexity of mining quantitative association rules.