Journal of Computer and System Sciences - 3rd Annual Conference on Structure in Complexity Theory, June 14–17, 1988
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Identifying the Minimal Transversals of a Hypergraph and Related Problems
SIAM Journal on Computing
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
On the complexity of dualization of monotone disjunctive normal forms
Journal of Algorithms
Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
Mining the most interesting rules
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Using association rules for product assortment decisions: a case study
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Transversing itemset lattices with statistical metric pruning
PODS '00 Proceedings of the nineteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Data mining: concepts and techniques
Data mining: concepts and techniques
Handbook of Theoretical Computer Science
Handbook of Theoretical Computer Science
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
On the Complexity of Mining Quantitative Association Rules
Data Mining and Knowledge Discovery
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Discovering All Most Specific Sentences by Randomized Algorithms
ICDT '97 Proceedings of the 6th International Conference on Database Theory
On Counting AC0 Circuits with Negative Constants
MFCS '98 Proceedings of the 23rd International Symposium on Mathematical Foundations of Computer Science
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th 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
On the Complexity of Generating Maximal Frequent and Minimal Infrequent Sets
STACS '02 Proceedings of the 19th Annual Symposium on Theoretical Aspects of Computer Science
On TC/sup 0/, AC/sup 0/, and Arithmetic Circuits
CCC '97 Proceedings of the 12th Annual IEEE Conference on Computational Complexity
Constraint-Based Rule Mining in Large, Dense Databases
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Towards multidimensional subspace skyline analysis
ACM Transactions on Database Systems (TODS)
Re-mining positive and negative association mining results
ICDM'10 Proceedings of the 10th industrial conference on Advances in data mining: applications and theoretical aspects
Re-mining item associations: Methodology and a case study in apparel retailing
Decision Support Systems
Mining top-k frequent closed itemsets is not in APX
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Computing frequent itemsets in parallel using partial support trees
PVM/MPI'05 Proceedings of the 12th European PVM/MPI users' group conference on Recent Advances in Parallel Virtual Machine and Message Passing Interface
Dimensions as Virtual Items: Improving the predictive ability of top-N recommender systems
Information Processing and Management: an International Journal
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Inducing association rules is one of the central tasks in data mining applications. Quantitative association rules induced from databases describe rich and hidden relationships to be found within data that can prove useful for various application purposes (e.g., market basket analysis, customer profiling, and others). Although association rules are quite widely used in practice, a thorough analysis of the related computational complexity is missing. This paper intends to provide a contribution in this setting. To this end, we first formally define quantitative association rule mining problems, which include boolean association rules as a special case; we then analyze computational complexity of such problems. The general problem as well as some interesting special cases are considered.