S. Parthasarathy, M. Zaki, M. Ogihara, and W. Li. Parallel data mining for association rules on shared-memory systems. Knowledge and Information Systems, 2001.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....a substantial amount of time. A number of efficient and scalable parallel formulations have been developed for finding frequent itemsets and sequences that are based on the candidate generation and counting framework [3, 18, 22, 16, 4] both for shared and distributed memory parallel computers [2, 22, 17, 8, 25, 29, 20]. However, the problem of parallelizing equivalence class based and projection based algorithms has received relatively little attention and existing parallel formulations for them have been targeted only toward shared memory architectures [30, 28] However, the irregular and unstructured nature ....

....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 ....

S. Parthasarathy, M. Zaki, M. Ogihara, and W. Li. Parallel data mining for association rules on shared-memory systems. Knowledge and Information Systems, 3(1):1--29, 2001.

....a substantial amount of time. A number of efficient and scalable parallel formulations have been developed for finding frequent itemsets and sequences that are based on the candidate generation and counting framework [3, 18, 22, 16, 4] both for shared and distributed memory 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 ....

....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 ....

S. Parthasarathy, M. Zaki, M. Ogihara, and W. Li. Parallel data mining for association rules on shared-memory systems. Knowledge and Information Systems, 3(1): 1-29, 2001.

....7.2. Previous Work Although the vertical layout has several positive features, as described above, it has received comparatively little attention in the data mining literature. In fact, to the best of our knowledge, it has been considered only in [13, 16] and a series of papers by Zaki et al. [18, 20, 19]. The vertical layout was first proposed in [13] which described how, with this layout, itemset supports can be counted using only the simple set operations of union and intersection. Subsequently, a graph based algorithm called DLG (Direct Large itemset Generation) was proposed in [16] These ....

....evaluation of these algorithms against classical horizontal layout algorithms such as Apriori [2] and Partition [15] indicated the potential for significant performance improvements. Finally, extensions of Eclat and Clique to shared memory parallel (SMP) database architectures were discussed in [18, 19]. While the above studies served to highlight the utility of the vertical approach, each has one or more of the following limitations (discussed in detail in Section 7.5) First, the candidate itemset pruning strategies are coarse grained. Second, the ability to handle large databases, especially ....

M. J. Zaki, M. Ogihara, S. Parthasarathy and W. Li. Parallel data mining for association rules on sharedmemory multi-processors. In Proc. of ACM SIGMOD Intl. Conf. on Management of Data, May 1996. 67

....dynamically change the database layout during the mining process, we assume that the initial database is always provided in the horizontal item list (IL) format. 2. 3 System Characteristics While there has been significant work in designing algorithms for the parallel mining of association rules [5, 11, 29, 18], in this study we focus on single processor environments. We also assume that the database is much larger than the available main memory. 3 1 2 3 4 TID 1 2 3 4 TID 1 2 3 4 TID ItemID ItemIDs 0 0 0 0 1 0 1 1 0 1 1 0 1 0 0 1 1 2 3 4 1 2 3 4 5 1 0 1 0 0 ....

S. Parthasarathy, M. J. Zaki, M. Ogihara, and W. Li. Parallel data mining for association rules on shared-memory systems. Knowledge and Information Systems, February 2001.

....achieved using our proposed optimizations. We also achieved good speed up for the parallel algorithm, but we observe a need for parallel I O techniques for further performance gains. A paper on this result has been accepted for publication in Supercomputing 96, Pittsburgh, Pennsylvania, Nov. 1996 [77]. In another piece of work, we look at the problem of compiler analysis for database programs. Most parallel databases exploit two types of parallelism: intra query parallelism and inter transaction concurrency. Between these two cases lies another type of parallelism: inter query parallelism ....

M. J. Zaki, M. Ogihara, S. Parthasarathy, and W. Li. Parallel data mining for association rules on shared-memory multi-processors. In Proceedings of Supercomputing '96, Pittsburgh, Pennsylvania, November 1996.

.... of the work has been on distributed memory machines, including association mining [1, 11, 12, 29] k means clustering [9] and decision tree classifiers [3, 10, 15, 24, 26] Recent efforts have also focused on shared memory parallelization of data mining algorithms, including association mining [28, 19, 20] and decision tree construction [27] Our work is significantly different, because we offer an interface and runtime support to parallelize a number of data mining algorithms. Our shared memory parallelization techniques are also significantly different, because we focus on a common framework for ....

M. J. Zaki, M. Ogihara, S. Parthasarathy, and W. Li. Parallel data mining for association rules on shared memory multiprocessors. In Proceedings of Supercomputing'96, November 1996.

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S. Parthasarathy, M. Zaki, M. Ogihara, and W. Li. Parallel data mining for association rules on shared-memory systems. Knowledge and Information Systems, 2001.

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M. J. Zaki, M. Ogihara, S. Parthasarathy, and W. Li. Parallel data mining for association rules on shared memory multiprocessors. In Proceedings of Supercomputing'96, November 1996.

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M. Zaki, M. Ogihara, S. Parthasarathy, and W. Li. Parallel data mining for association rules on shared-memory multi-processors. In Proc. of Supercomputing '96, Pittsburg, 1996.

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S. Parthasarathy, M. Zaki, M. Ogihara, and W. Li. Parallel data mining for association rules on shared-memory systems. In Knowledge and Information Systems, Santa Barbara, CA, February 2001.

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M. J. Zaki, M. Ogihara, S. Parthasarathy, and W. Li. Parallel data mining for association rules on shared memory multiprocessors. In Proceedings of Supercomputing'96, November 1996.

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S. Parthasarathy, M. Zaki, M. Ogihara, and W. Li. Parallel data mining for association rules on shared-memory systems. Knowledge and Information Systems, 3(1):1--29, 2001.

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S. Parthasarathy, M. Zaki, M. Ogihara, and W. Li. Parallel data mining for association rules on shared-memory systems. Knowledge and Information Systems, 3(1):1--29, 2001.

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M. J. Zaki, M. Ogihara, S. Parthasarathy, and W. Li. Parallel data mining for association rules on shared-memory multi-processors. In Supercomputing'96, Pittsburg, PA, November 1996. 15

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S. Parthasarathy, M. J. Zaki, and M. Ogihara. Parallel data mining for association rules on sharedmemory systems. Knowledge and Information Systems: An International Journal, 3(1):1--29, February 2001.

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M.J. Zaki, M. Ogihara, S. Parthasarathy, and W. Li, "Parallel Data Mining for Association Rules on Shared Memory Multiprocessors, " Proc. Conf. Supercomputing '96, Nov. 1996.

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S. Parthasarathy, M. Zaki, M. Ogihara, and W. Li, "Parallel Data Mining for Association Rules on Shared-Memory Systems," Knowledge and Information Systems, to appear, 2000.

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M. J. Zaki, M. Ogihara, S. Parthasarathy, and W. Li. Parallel data mining for association rules on shared memory multiprocessors. In Proceedings of Supercomputing'96,November 1996.

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M. J. Zaki, M. Ogihara, S. Parthasarathy, and W. Li. Parallel data mining for association rules on shared memory multiprocessors. In Proceedings of Supercomputing'96, November 1996.

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M. Zaki, M. Ogihara, S. Parthasarathy, and W. Li. Parallel Data Mining for Association Rules on Shared-Memory Multiprocessors. In Proceedings of Supercomputing'96, pages 17--22, Pittsburg, PA, November 1996.

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S. Parthasarathy, M. J. Zaki, M. Ogihara, and W. Li. Parallel Data Mining for Association Rules on Shared-Memory Systems. Knowledge and Information Systems, 3(1):1--29, 2001.

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M. J. Zaki, M. Ogihara, S. Parthasarathy, and W. Li. Parallel data mining for association rules on shared-memory multi-processors. In Proc. ACM/IEEE Conf. Supercomputing, 1996.

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M. J. Zaki, M. Ogihara, S. Parthasarathy, and W. Li " Parallel Data Mining for Association Rules on Shared-Memory Multi-Processors," Proc. ACM/IEEE conf. Supercomputing, July 1996.

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M. J. Zaki, M. Ogihara, S. Parthasarathy, and W. Li, Parallel data mining for association rules on shared-memory multi-processors. Technical Report 618, Computer Science Department, The University of Rochester, May 1996. 35

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M.J.Zaki,M.Ogihara,S.Parthasarathy,andW.Li,"ParallelDataMining for Association Rules on Shared-memory Multi-processors," Proceedings of the Supercomputing Conference, Pittsburgh, PA, November 1996.

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