Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 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
Parallel and Distributed Association Mining: A Survey
IEEE Concurrency
H-Mine: Hyper-Structure Mining of Frequent Patterns in Large Databases
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Fast Parallel Association Rule Mining without Candidacy Generation
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
SQL Based Association Rule Mining Using Commercial RDBMS (IBM DB2 UBD EEE)
DaWaK 2000 Proceedings of the Second International Conference on Data Warehousing and Knowledge Discovery
MAFIA: A Maximal Frequent Itemset Algorithm
IEEE Transactions on Knowledge and Data Engineering
An implementation of the FP-growth algorithm
Proceedings of the 1st international workshop on open source data mining: frequent pattern mining implementations
Frequent pattern mining: current status and future directions
Data Mining and Knowledge Discovery
Computing frequent itemsets inside oracle 10G
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Optimization of frequent itemset mining on multiple-core processor
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Pfp: parallel fp-growth for query recommendation
Proceedings of the 2008 ACM conference on Recommender systems
Some Observations of Sequential, Parallel and Distributed Association Rule Mining Algorithms
ICCAE '09 Proceedings of the 2009 International Conference on Computer and Automation Engineering
Parallel and Distributed Frequent Pattern Mining in Large Databases
HPCC '09 Proceedings of the 2009 11th IEEE International Conference on High Performance Computing and Communications
Tree partition based parallel frequent pattern mining on shared memory systems
IPDPS'06 Proceedings of the 20th international conference on Parallel and distributed processing
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Frequent pattern mining is an important problem in data mining with many practical applications. Current parallel methods for mining frequent patterns unstably perform for different database types and under-utilize the benefits of multi-core shared memory machines. We present ShaFEM, a novel parallel frequent pattern mining method, to address these issues. Our method can dynamically adapt to the data characteristics to efficiently perform on both sparse and dense databases. Its parallel mining lock free approach minimizes the synchronization needs and maximizes the data independence to enhance the scalability. Its structure lends itself well for dynamic job scheduling resulting in well-balanced load on new multi-core shared memory architectures. We evaluate ShaFEM on a 12-core multi-socket server and find that our method runs 2.1--5.8 times faster than the state-of-the-art parallel method. For some test cases, we have shown that ShaFEM saves 4.9 days and 12.8 hours of execution time over the compared method.