Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Multilevel hypergraph partitioning: application in VLSI domain
DAC '97 Proceedings of the 34th annual Design Automation Conference
Growing decision trees on support-less association rules
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Building Hierarchical Classifiers Using Class Proximity
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
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
Constraint-Based Rule Mining in Large, Dense Databases
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Finding Interesting Associations without Support Pruning
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
Mining Strong Affinity Association Patterns in Data Sets with Skewed Support Distribution
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
A fuzzy logic based method to acquire user threshold of minimum-support for mining association rules
Information Sciences—Informatics and Computer Science: An International Journal
Divide-and-Approximate: A Novel Constraint Push Strategy for Iceberg Cube Mining
IEEE Transactions on Knowledge and Data Engineering
TFP: An Efficient Algorithm for Mining Top-K Frequent Closed Itemsets
IEEE Transactions on Knowledge and Data Engineering
TAPER: A Two-Step Approach for All-Strong-Pairs Correlation Query in Large Databases
IEEE Transactions on Knowledge and Data Engineering
Mining maximal hyperclique pattern: A hybrid search strategy
Information Sciences: an International Journal
Efficient association rule mining among both frequent and infrequent items
Computers & Mathematics with Applications
A review of associative classification mining
The Knowledge Engineering Review
Just enough learning (of association rules): the TAR2 "Treatment" learner
Artificial Intelligence Review
Association rule and quantitative association rule mining among infrequent items
Proceedings of the 8th international workshop on Multimedia data mining: (associated with the ACM SIGKDD 2007)
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks, Part II
Expert Systems with Applications: An International Journal
Finding sporadic rules in the diagnosis of the Erythemato-Squamous diseases
Intelligent Data Analysis
On Optimal Rule Mining: A Framework and a Necessary and Sufficient Condition of Antimonotonicity
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Summary queries for frequent itemsets mining
Journal of Systems and Software
Towards the web of concepts: extracting concepts from large datasets
Proceedings of the VLDB Endowment
A new approach of inventory classification based on loss profit
Expert Systems with Applications: An International Journal
Mining top-k regular-frequent itemsets using database partitioning and support estimation
Expert Systems with Applications: An International Journal
Mining classification rules without support: an anti-monotone property of Jaccard measure
DS'11 Proceedings of the 14th international conference on Discovery science
Is frequency enough for decision makers to make decisions?
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Optimonotone Measures For Optimal Rule Discovery
Computational Intelligence
A prediction framework based on contextual data to support Mobile Personalized Marketing
Decision Support Systems
Learning theory analysis for association rules and sequential event prediction
The Journal of Machine Learning Research
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An open problem is to find all rules that satisfy a minimum confidence but not necessarily a minimum support. Without the support requirement, the classic support-based pruning strategy is inapplicable. The problem demands a confidence-based pruning strategy. In particular, the following monotonicity of confidence, called the universal-existential upward closure, holds: if a rule of size k is confident (for the given minimum confidence), for every other attribute not in the rule, some specialization of size k+1 using the attribute must be confident. Like the support-based pruning, the bottleneck is at the memory that often is too small to store the candidates required for search. We implement this strategy on disk and study its performance.