Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Fast discovery of association rules
Advances in knowledge discovery and data mining
KDD-Cup 2000 organizers' report: peeling the onion
ACM SIGKDD Explorations Newsletter - Special issue on “Scalable data mining algorithms”
Real world performance of association rule algorithms
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Scalable Algorithms for Association Mining
IEEE Transactions on Knowledge and Data Engineering
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
Fast vertical mining using diffsets
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Cache-conscious frequent pattern mining on a modern processor
VLDB '05 Proceedings of the 31st international conference on Very large data bases
UDM '05 Proceedings of the International Workshop on Ubiquitous Data Management
Mining association rules in very large clustered domains
Information Systems
Mining top-k frequent patterns in the presence of the memory constraint
The VLDB Journal — The International Journal on Very Large Data Bases
DRFP-tree: disk-resident frequent pattern tree
Applied Intelligence
Associative classification with artificial immune system
IEEE Transactions on Evolutionary Computation
Optimal constraint-based decision tree induction from itemset lattices
Data Mining and Knowledge Discovery
Effective sentiment stream analysis with self-augmenting training and demand-driven projection
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
An efficient real-time frequent pattern mining technique using diff-sets
ICCS'05 Proceedings of the 5th international conference on Computational Science - Volume Part III
Memory-aware frequent k-itemset mining
KDID'05 Proceedings of the 4th international conference on Knowledge Discovery in Inductive Databases
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During the past decade, many algorithms have been proposed to solve the frequent itemset mining problem, i.e. find all sets of items that frequently occur together in a given database of transactions. Although very efficient techniques have been presented, they still suffer from the same problem. That is, they are all inherently dependent on the amount of main memory available. Moreover, if this amount is not enough, the presented techniques are simply not applicable anymore, or significantly need to pay in performance. In this paper, we give a rigorous comparison between current state of the art techniques and present a new and simple technique, based on sorting the transaction database, resulting in a sometimes more efficient algorithm for frequent itemset mining using less memory.