Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Scalable Algorithms for Association Mining
IEEE Transactions on Knowledge and Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th 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
Static load balancing of parallel mining of frequent itemsets using reservoir sampling
MLDM'11 Proceedings of the 7th international conference on Machine learning and data mining in pattern recognition
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This paper presents improvements of the Parallel-FIMI method for statical load balancing of mining of all frequent itemsets on a distributed-memory (DM) parallel machine. This method probabilistically partitions the space of all frequent itemsets into partitions of approximately the same size. The improvements consist in paralelization of the approximate partitioning of the search space and of dynamic reordering of items during construction of prefix-based equivalence classes. The new versions of the method achieve nearly linear speedups up to 10 processors.