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
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
MAFIA: A Maximal Frequent Itemset Algorithm for Transactional Databases
Proceedings of the 17th International Conference on Data Engineering
An efficient parallel and distributed algorithm for counting frequent sets
VECPAR'02 Proceedings of the 5th international conference on High performance computing for computational science
An efficient algorithm for enumerating pseudo cliques
ISAAC'07 Proceedings of the 18th international conference on Algorithms and computation
Mining statistically important equivalence classes and delta-discriminative emerging patterns
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Companion to the 22nd ACM SIGPLAN conference on Object-oriented programming systems and applications companion
AusDM '07 Proceedings of the sixth Australasian conference on Data mining and analytics - Volume 70
Constraint programming for itemset mining
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
An effective algorithm for mining 3-clusters in vertically partitioned data
Proceedings of the 17th ACM conference on Information and knowledge management
Closed patterns meet n-ary relations
ACM Transactions on Knowledge Discovery from Data (TKDD)
Estimating the number of frequent itemsets in a large database
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
Applying bit-vector projection approach for efficient mining of N-most interesting frequent itemsets
CI '07 Proceedings of the Third IASTED International Conference on Computational Intelligence
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
BISC: A bitmap itemset support counting approach for efficient frequent itemset mining
ACM Transactions on Knowledge Discovery from Data (TKDD)
Knowledge Compilation for Itemset Mining
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Fun at a department store: data mining meets switching theory
FUN'10 Proceedings of the 5th international conference on Fun with algorithms
Integrating constraint programming and itemset mining
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part II
Itemset mining: A constraint programming perspective
Artificial Intelligence
LGM: mining frequent subgraphs from linear graphs
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part II
Semi-supervised learning for mixed-type data via formal concept analysis
ICCS'11 Proceedings of the 19th international conference on Conceptual structures for discovering knowledge
Automatic construction of static evaluation functions for computer game players
DS'06 Proceedings of the 9th international conference on Discovery Science
Efficient mining of large maximal bicliques
DaWaK'06 Proceedings of the 8th international conference on Data Warehousing and Knowledge Discovery
Top-N minimization approach for indicative correlation change mining
MLDM'12 Proceedings of the 8th international conference on Machine Learning and Data Mining in Pattern Recognition
Cover similarity based item set mining
Bisociative Knowledge Discovery
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Mining frequent itemsets in data streams within a time horizon
Data & Knowledge Engineering
Para Miner: a generic pattern mining algorithm for multi-core architectures
Data Mining and Knowledge Discovery
A time-efficient breadth-first level-wise lattice-traversal algorithm to discover rare itemsets
Data Mining and Knowledge Discovery
International Journal of Knowledge Discovery in Bioinformatics
Semi-supervised learning on closed set lattices
Intelligent Data Analysis
Hi-index | 0.01 |
For a transaction database, a frequent itemset is an itemset included in at least a specified number of transactions. To find all the frequent itemsets, the heaviest task is the computation of frequency of each candidate itemset. In the previous studies, there are roughly three data structures and algorithms for the computation: bitmap, prefix tree, and array lists. Each of these has its own advantage and disadvantage with respect to the density of the input database. In this paper, we propose an efficient way to combine these three data structures so that in any case the combination gives the best performance.