O. Zaiane, M. E1-Hajj, and P. Lu. Fast parallel association rule mining without candidacy generation. In IEEE International Conference on Data Mining, pages 6654568, 2001. Home/Search Document Details and Download
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Parallel Formulations of Tree-Projection-Based Sequence.. - Guralnik, Karypis (Correct)
.... parallel computers [2, 22, 17, 8, 25, 29, 20] However, the problem of parallelizing equivalence class based and projection based algorithms has received relatively litfie attention and existing parallel formulations for them have been targeted only toward shared memory architectures [30, 28]. However, the irregular and unstructured nature of the task graph that these algorithms generate and the fact that these tasks operate on overlapping subdatabases makes it challenging to develop efficient and scalable parallel formulations for distributed memory parallel computers. This paper ....
....original database. The basic ideas in this algorithm were recently used to develop a similar algorithm for finding sequential patterns [21] A number of parallel frequent itemset discovery algorithms have been developed that focus on parallelizing the various serial algorithms for that problem [2, 22, 17, 8, 25, 29, 20, 28]. Depending on the nature of the underlying serial algorithm, many of these approaches follow the same parallelization strategy. Providing exact details on all of these algorithms is beyond the scope of this section, and for this reason we only focus on the various issues involved in parallelizing ....
O. Zaiane, M. E1-Hajj, and P. Lu. Fast parallel association rule mining without candidacy generation. In IEEE International Conference on Data Mining, pages 6654568, 2001.
Fast Parallel Association Rule Mining Without Candidacy.. - Osmar Zaane Mohammad (1 citation) Self-citation (Zaane El-hajj Lu) (Correct)
....Association Rule Mining Without Candidacy Generation Osmar R. Zaane Mohammad El Hajj Paul Lu University of Alberta, Edmonton, Alberta, Canada zaiane, mohammad, paullu cs.ualberta.ca Abstract In this paper we introduce a new parallel algorithm MLFPT (Multiple Local Frequent Pattern Tree) [11] for parallel mining of frequent patterns, based on FP growth mining, that uses only two full I O scans of the database, eliminating the need for generating the candidate items, and distributing the work fairly among processors. We have devised partitioning strategies at different stages of the ....
O. R. Zaane, M. El-Hajj, and P. Lu. Fast parallel association rule mining without candidacy generation. Technical Report TR01-12, Department of Computing Science, University of Alberta, Canada, August
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