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
MAFIA: A Maximal Frequent Itemset Algorithm for Transactional Databases
Proceedings of the 17th International Conference on Data Engineering
Efficiently Mining Maximal Frequent Itemsets
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
CLOSET+: searching for the best strategies for mining frequent closed itemsets
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Fast vertical mining using diffsets
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Bifold Constraint-Based Mining by Simultaneous Monotone and Anti-Monotone Checking
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Implementing leap traversals of the itemset lattice
Proceedings of the 1st international workshop on open source data mining: frequent pattern mining implementations
Parallel Leap: Large-Scale Maximal Pattern Mining in a Distributed Environment
ICPADS '06 Proceedings of the 12th International Conference on Parallel and Distributed Systems - Volume 1
Parallel Bifold: Large-scale parallel pattern mining with constraints
Distributed and Parallel Databases
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Regardless of the frequent patterns to discover, either the full frequent patterns or the condensed ones, either closed or maximal, the strategy always includes the traversal of the lattice of candidate patterns. We study the existing depth versus breadth traversal approaches for generating candidate patterns and propose in this paper a new traversal approach that jumps in the search space among only promising nodes. Our leaping approach avoids nodes that would not participate in the answer set and reduce drastically the number of candidate patterns. We use this approach to efficiently pinpoint maximal patterns at the border of the frequent patterns in the lattice and collect enough information in the process to generate all subsequent patterns.