D. Cheung, K. Hu, and S. Xia. Asynchronous parallel algorithm for mining association rules on sharedmemory multi-processors. In 10th ACM Symp. Parallel Algorithms and Architectures, pages 279--228, New York, 1998. Home/Search Document Details and Download
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Data Allocation Algorithm for Parallel Association Rule Discovery - Manning, Keane (1 citation) (Correct)
....virtual machine [5] and FPM was written in JAVA and JPVM. The number of passes of FPM over the database vary from 2 for datasets with 85 sparsity to 10 for those with 25 and it is not easy to compare the e ect of DAA. The second pass often has the highest computational cost of all passes [3] and it is therefore particularly important to focus attention on the e ect of DAA over the rst two passes of FPM. Execution times were taken for the rst two passes over 10 support counts across 4 processors and the average percentage reduction in execution time for each pass is shown in Table ....
D. W. Cheung, K. Hu, and S. Xia. Asynchronous parallel algorithm for mining association rules on a shared-memory multi-processors'. In proceedings of the 10th Annual ACM Symposium on Parallel Algorithms and Architectures (SPAA), June 1998.
Efficiently Mining Approximate Models of.. - Veloso.. (2002) (Correct)
.... often for market basket data analysis, but more recently it has also been used in such far reaching domains as bioinformatics [7] text mining [14] and scientific computing [9] Previous research efforts have produced many efficient sequential algorithms[6,1,8,18,19] several parallel algorithms[20,13,3], and a few incremental algorithms for determining associations [16,15,2,4] The majority of the incremental algorithms studied employ specific data structures to maintain the information previously mined so that it can be augmented by the updates. These techniques are designed to produce exact ....
D. Cheung, K. Hu, and S. Xia. Asynchronous parallel algorithm for mining association rules on a shared-memory multipprocessors. In ACM Symposium on Parallel Algorithms
Knowledge Discovery From Distributed And Textual Data - Cho (1999) (1 citation) (Correct)
....so that only itemsets found large in the previous pass are considered. This makes the number of candidate itemsets much smaller and the resulting algorithm Apriori and AprioriTid are much more efficient. Besides the association rule generation from a central transaction database, Cheung et al. [23, 25] also investigate distributed repositories and suggest parallel mining to generate association rules. Along with previous effort done, Cheung concentrates on 46 the issue of generating those large itemsets in a distributed environment with shared memory. He proposes an asynchronous parallel ....
Cheung D., Hu K. and Xia S., "Asynchronous Parallel Algorithm for Mining Association Rules on a Shared-memory Multi-processors", Proc. The Tenth Annual ACM Symposium on Parallel Algorithms And Architectures (SPAA98) , Puerto Vallarta, Mexico, June, 1998.
Parallel Data Mining for Association Rules on.. - Parthasarathy, Zaki.. (2000) (5 citations) (Correct)
....optimizations like hash tree balancing, parallel candidate generation, and short circuited subset checking. We further studied the locality and false sharing problems encountered in CCPD, and solutions for alleviating them. Parts of this paper have appeared in [44, 35] A recent paper presents APM [13], an asynchronous parallel algorithm for shared memory machines based on the DIC algorithm [10] Our proposed optimizations, and locality enhancing and false sharing reducing policies, are orthogonal to their approach. 26 Parthasarathy et al. 7.2. Improving Locality Several automatic techniques ....
D. Cheung, K. Hu, and S. Xia. Asynchronous parallel algorithm for mining association rules on shared-memory multi-processors. In 10th ACM Symp. Parallel Algorithms and Architectures, June 1998.
Parallel Data Mining for Association Rules on Shared-memory.. - Zaki, al. (1998) (30 citations) (Correct)
....candidate generation, and short circuited subset checking. We further studied the locality and false sharing problems encountered in CCPD, and solutions for alleviating them. Parts of this paper have appeared in (Zaki et al. 1996; Parthasarathy, Zaki, Li 1998) A recent paper presents APM (Cheung, Hu, Xia 1998), an asynchronous parallel algorithm for shared memory machines based on the DIC algorithm (Brin et al. 1997) Our proposed optimizations, and locality enhancing and false sharing reducing policies, are orthogonal to their approach. 7.2. Improving Locality Several automatic techniques like ....
Cheung, D.; Hu, K.; and Xia, S. 1998. Asynchronous parallel algorithm for mining association rules on shared-memory multi-processors. In 10th ACM Symp. Parallel Algorithms and Architectures.
Parallel and Distributed Association Mining: A Survey - Zaki (1999) (37 citations) (Correct)
....locality. They proposed an effective privatization mechanism, where each processor collects counts in a local array, followed by a sum reduction, for reducing false sharing. Experiments on a 12 node SGI Challenge showed improvements of 50 60 over the base case. 4.2. 2 DIC based Cheung et al. [14] have proposed the Asynchronous Parallel Mining (APM) algorithm, which is based on DIC. APM uses the global pruning technique of FDM to reduce the size of candidate 2 itemsets. This pruning is most effective when there is high data skew among the partitions. However, DIC requires that the ....
D. Cheung, K. Hu, and S. Xia. Asynchronous parallel algorithm for mining association rules on shared-memory multi-processors. In 10th ACM Symp. Parallel Algorithms and Architectures, June 1998.
Fast Parallel Association Rule Mining without Candidacy.. - Zaïane, El-Hajj, Lu (2001) (Correct)
No context found.
D. Cheung, K. Hu, and S. Xia. Asynchronous parallel algorithm for mining association rules on sharedmemory multi-processors. In 10th ACM Symp. Parallel Algorithms and Architectures, pages 279--228, New York, 1998.
Efficiently Mining Approximate Models of.. - Veloso, Gusmao.. (2002) (Correct)
No context found.
D. Cheung, K. Hu, and S. Xia. Asynchronous parallel algorithm for mining association rules on a shared-memory multipprocessors. In ACM Symposium on Parallel Algorithms and Architectures, pages 279--288, 1998. Veloso et al.
Load Balancing on PC Clusters with the Super-Programming Model - Jin, Ziavras (2003) (Correct)
No context found.
D.W. Cheung, K. Hu, and S. Xia, "Asynchronous Parallel Algorithm for Mining Association Rules on a SharedMemory Multi-Processors ," Proc. 10 annual ACM symp. Paral. Alg. Archit., p279-288, 1998.
Parallel and Distributed Association Mining: A Survey - Zaki (1999) (37 citations) (Correct)
No context found.
D. Cheung, K. Hu, and S. Xia, "Asynchronous Parallel Algorithm for Mining Association Rules on Shared-Memory MultiProcessors, " Proc. 10th ACM Symp. Parallel Algorithms and Architectures, ACM Press, New York, 1998, pp. 279--288.
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