D.B. Skillicorn. Strategies for parallelizing data mining. In Proceedings of the Workshop on HighPerformance Data Mining, in association with IPPS/SPDP 1998. Home/Search Document Details and Download
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A Middleware for Developing Parallel Data Mining Applications - Jin, Agrawal (2001) (1 citation) (Correct)
....time I O Time Figure 8: Performance of k Nearest Neighbor 14 results on an SMP machine, it is using MPI) Second, I O is handled explicitly by the programmers in their approach. The similarity among parallel versions of different data mining techniques have also been observed by Skillicorn [37, 36]. Our work is different in offering a middleware to exploit the similarity, and ease parallel application development. The challenges in scalable and parallel data mining we listed in Section 1 have also been observed by a number of other authors [5, 13, 21, 26, 28, 31, 36] Several runtime ....
D.B. Skillicorn. Strategies for parallelizing data mining. In Proceedings of the Workshop on High-Performance Data Mining, in association with IPPS/SPDP
An Efficient Association Mining Implementation on Cluster of SMPs - Jin, Agrawal (2001) (Correct)
....(while they report results on an SMP machine, it is using MPI) Second, I O is handled explicitly by the programmers in their approach. The similarity among parallel versions of different data mining techniques, which motivated the design of our middleware, has also been observed by Skillicorn [26, 25]. Several runtime support libraries and file systems have been developed to support efficient I O in a parallel environment [11, 23] most noticeable among these is the PASSION library designed by Alok Choudhary s group [27, 28] They usually provide a collective I O interface, in which all ....
D.B. Skillicorn. Strategies for parallelizing data mining. In Proceedings of the Workshop on High-Performance Data Mining, in association with IPPS/SPDP
Compiler and Middleware Support for Scalable Data Mining - Agrawal, Jin, Li (Correct)
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D.B. Skillicorn. Strategies for parallelizing data mining. In Proceedings of the Workshop on HighPerformance Data Mining, in association with IPPS/SPDP 1998.
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