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
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Hash based parallel algorithms for mining association rules
DIS '96 Proceedings of the fourth international conference on on Parallel and distributed information systems
Parallel Mining of Association Rules
IEEE Transactions on Knowledge and Data Engineering
Fast Parallel Association Rule Mining without Candidacy Generation
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Dynamic Load Balancing for Parallel Association Rule Mining on Heterogenous PC Cluster Systems
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Performance Evaluation of Distributed Algorithms for Mining Association Rules on Workstation Cluster
ICPP '00 Proceedings of the 2000 International Workshop on Parallel Processing
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One of the best-known problems in data mining is association rule mining. It requires very large computation and I/O traffic capacity, therefore several distributed and parallel association rule mining algorithms have been developed. However the association rule mining problem is NP complete, the execution time estimation of the algorithms can be very important, especially for load balancing or for capacity and resource planning. In this paper a novel execution time prediction method is introduced and evaluated on a PC cluster environment. The average relative error of this model is less than 10 percent.