Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Exploratory mining and pruning optimizations of constrained associations rules
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Efficiently mining long patterns from databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Efficient mining of association rules using closed itemset lattices
Information Systems
Generating non-redundant association rules
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Analysis of recommendation algorithms for e-commerce
Proceedings of the 2nd ACM conference on Electronic commerce
Mining frequent patterns by pattern-growth: methodology and implications
ACM SIGKDD Explorations Newsletter - Special issue on “Scalable data mining algorithms”
Formal Concept Analysis: Mathematical Foundations
Formal Concept Analysis: Mathematical Foundations
Data Mining Techniques: For Marketing, Sales, and Customer Support
Data Mining Techniques: For Marketing, Sales, and Customer Support
Constraint-Based Rule Mining in Large, Dense Databases
Data Mining and Knowledge Discovery
E-Commerce Recommendation Applications
Data Mining and Knowledge Discovery
Free-Sets: A Condensed Representation of Boolean Data for the Approximation of Frequency Queries
Data Mining and Knowledge Discovery
Mining Multiple-Level Association Rules in Large Databases
IEEE Transactions on Knowledge and Data Engineering
Discovering Frequent Closed Itemsets for Association Rules
ICDT '99 Proceedings of the 7th International Conference on Database Theory
Mining All Non-derivable Frequent Itemsets
PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Dataless Transitions Between Concise Representations of Frequent Patterns
Journal of Intelligent Information Systems
DBC: a condensed representation of frequent patterns for efficient mining
Information Systems
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Mining Non-Redundant Association Rules
Data Mining and Knowledge Discovery
Taxonomy-driven computation of product recommendations
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Generating a Condensed Representation for Association Rules
Journal of Intelligent Information Systems
Knowledge discovery by probabilistic clustering of distributed databases
Data & Knowledge Engineering
Data Mining and Knowledge Discovery
Deriving non-redundant approximate association rules from hierarchical datasets
Proceedings of the 17th ACM conference on Information and knowledge management
Establishing relationships among patterns in stock market data
Data & Knowledge Engineering
Non-redundant sequential rules-Theory and algorithm
Information Systems
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
Essential patterns: a perfect cover of frequent patterns
DaWaK'05 Proceedings of the 7th international conference on Data Warehousing and Knowledge Discovery
A survey on condensed representations for frequent sets
Proceedings of the 2004 European conference on Constraint-Based Mining and Inductive Databases
Mining association rules with improved semantics in medical databases
Artificial Intelligence in Medicine
Arbitrarily distributed data-based recommendations with privacy
Data & Knowledge Engineering
Conceptual modeling of cardinality constraints in social publishing
International Journal of Intelligent Systems
Extraction of fuzzy rules from fuzzy decision trees: An axiomatic fuzzy sets (AFS) approach
Data & Knowledge Engineering
BruteSuppression: a size reduction method for Apriori rule sets
Journal of Intelligent Information Systems
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Association rule mining has contributed to many advances in the area of knowledge discovery. However, the quality of the discovered association rules is a big concern and has drawn more and more attention recently. One problem with the quality of the discovered association rules is the huge size of the extracted rule set. Often for a dataset, a huge number of rules can be extracted, but many of them can be redundant to other rules and thus useless in practice. Mining non-redundant rules is a promising approach to solve this problem. In this paper, we first propose a definition for redundancy, then propose a concise representation, called a Reliable basis, for representing non-redundant association rules. The Reliable basis contains a set of non-redundant rules which are derived using frequent closed itemsets and their generators instead of using frequent itemsets that are usually used by traditional association rule mining approaches. An important contribution of this paper is that we propose to use the certainty factor as the criterion to measure the strength of the discovered association rules. Using this criterion, we can ensure the elimination of as many redundant rules as possible without reducing the inference capacity of the remaining extracted non-redundant rules. We prove that the redundancy elimination, based on the proposed Reliable basis, does not reduce the strength of belief in the extracted rules. We also prove that all association rules, their supports and confidences, can be retrieved from the Reliable basis without accessing the dataset. Therefore the Reliable basis is a lossless representation of association rules. Experimental results show that the proposed Reliable basis can significantly reduce the number of extracted rules. We also conduct experiments on the application of association rules to the area of product recommendation. The experimental results show that the non-redundant association rules extracted using the proposed method retain the same inference capacity as the entire rule set. This result indicates that using non-redundant rules only is sufficient to solve real problems needless using the entire rule set.