Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Pruning and summarizing the discovered associations
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Turbo-charging vertical mining of large databases
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Generating non-redundant association rules
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Real world performance of association rule algorithms
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Using a Hash-Based Method with Transaction Trimming for Mining Association Rules
IEEE Transactions on Knowledge and Data Engineering
Finding Interesting Associations without Support Pruning
IEEE Transactions on Knowledge and Data Engineering
Pincer-Search: An Efficient Algorithm for Discovering the Maximum Frequent Set
IEEE Transactions on Knowledge and Data Engineering
On the Discovery of Interesting Patterns in Association Rules
VLDB '98 Proceedings of the 24rd 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
A simple algorithm for finding frequent elements in streams and bags
ACM Transactions on Database Systems (TODS)
An efficient algorithm for mining closed inter-transaction itemsets
Data & Knowledge Engineering
Towards personalized recommendation by two-step modified Apriori data mining algorithm
Expert Systems with Applications: An International Journal
Depth first generation of frequent patterns without candidate generation
PAKDD'07 Proceedings of the 2007 international conference on Emerging technologies in knowledge discovery and data mining
Top-down and bottom-up strategies for incremental maintenance of frequent patterns
PAKDD'07 Proceedings of the 2007 international conference on Emerging technologies in knowledge discovery and data mining
Application of KDD in mechanical structure symmetry design
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 7
A fast pruning redundant rule method using Galois connection
Applied Soft Computing
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A well-known problem that limits the practical usage of association rule mining algorithms is the extremely large number of rules generated. Such a large number of rules makes the algorithms inefficient and makes it difficult for the end users to comprehend the discovered rules. We present the concept of a heavy itemset. An itemset A is heavy (for given support and confidence values) if all possible association rules made up of items only in A are present. We prove a simple necessary and sufficient condition for an itemset to be heavy. We present a formula for the number of possible rules for a given heavy itemset, and show that a heavy itemset compactly represents an exponential number of association rules. Along with two simple search algorithms, we present an efficient greedy algorithm to generate a collection of disjoint heavy itemsets in a given transaction database. We then present a modified apriori algorithm that starts with a given collection of disjoint heavy itemsets and discovers more heavy itemsets, not necessarily disjoint with the given ones.