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
Efficiently mining long patterns from databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
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
Depth first generation of long patterns
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Algorithms for association rule mining — a general survey and comparison
ACM SIGKDD Explorations Newsletter
Maintenance of Discovered Association Rules in Large Databases: An Incremental Updating Technique
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
MAFIA: A Maximal Frequent Itemset Algorithm for Transactional Databases
Proceedings of the 17th International Conference on Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
A General Incremental Technique for Maintaining Discovered Association Rules
Proceedings of the Fifth International Conference on Database Systems for Advanced Applications (DASFAA)
Mining Association Rules in Text Databases Using Multipass with Inverted Hashing and Pruning
ICTAI '02 Proceedings of the 14th IEEE International Conference on Tools with Artificial Intelligence
New Algorithms for Fast Discovery of Association Rules
New Algorithms for Fast Discovery of Association Rules
Fast Algorithms for Frequent Itemset Mining Using FP-Trees
IEEE Transactions on Knowledge and Data Engineering
GenMax: An Efficient Algorithm for Mining Maximal Frequent Itemsets
Data Mining and Knowledge Discovery
CanTree: A Tree Structure for Efficient Incremental Mining of Frequent Patterns
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Tree-based partitioning of date for association rule mining
Knowledge and Information Systems
Association mining in time-varying domains
Intelligent Data Analysis
Frequent pattern mining: current status and future directions
Data Mining and Knowledge Discovery
A New Method for Incremental Updating Frequent Patterns Mining
ICICIC '07 Proceedings of the Second International Conference on Innovative Computing, Informatio and Control
Approximate mining of frequent patterns on streams
Intelligent Data Analysis - Knowlegde Discovery from Data Streams
An efficient incremental mining algorithm-QSD
Intelligent Data Analysis
CAR-NF: A classifier based on specific rules with high netconf
Intelligent Data Analysis
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In this paper, two algorithms for mining frequent itemsets in large sparse datasets are proposed. The first one, named Compressed Arrays (CA), allows to process datasets that do not change along the time (static datasets) while the second one, based on the ideas of the former and named Dynamic Compressed Arrays (DCA), processes datasets that change along the time by adding/deleting transactions (dynamic datasets). Both algorithms introduce a novel way to use equivalence classes of itemsets by performing a breadth first search through them and by storing the class prefix support in compressed arrays, which allows fast itemset support computing. On the other hand, unlike previous algorithms for dynamic datasets that store the full dataset in main memory without reusing the current frequent itemsets, DCA algorithm stores the current frequent itemsets in binary files, grouped in equivalence classes, and reuses them to calculate the new frequent itemsets.