J. Chattratichat, etc., Large scale data mining: challenges and responses. In: Proceedings of International Conference on Knowledge Discovery and Data Mining, 1997: 143-146.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....dimensionality reduction. Implementation of neural networks [19,22] or genetic algorithms [1,7,13,24] have proved to be computationally eective in some application domains. Parallel data mining provides solutions by utilizing the large data retrieval and processing power of parallel architecture [4]. Classication tree structured algorithms, like C4.5 [18] on the other hand, use information theory based recursive partitioning, divide and conquer algorithms, to split the data sets iteratively, by which to determine the comparative importance of selected variables in reducing the ....

J. Chattratichat et al., Large scale data mining challenges and responses, in: Proceedings of the Third International Conference on Knowledge Discovery and Data Mining, AAAI Press, Menlo Park, CA, 1997.

....slower, it is appealing to employ parallel computing and to exploit the main memory of all the processors. Parallel data mining algorithms have been recently considered for tasks such as association rules and classi cation, see, for example, Agrawal and Shafer [1] Chattratichat et al. [2], Cheung and Xiao [3] Han, Karypis, and Kumar [4] 2 Dhillon Modha Joshi, Karypis, and Kumar [5] Kargupta, Hamzaoglu, and Sta ord [6] Shafer, Agrawal, and Mehta [7] Srivastava, et al. 8] Zaki, Ho, and Agrawal [9] and Zaki et al. 10] Also, see Stolorz and Musick [11] and Freitas and ....

Chattratichat, J., Darlington, J., Ghanem, M., Guo, Y., Huning, H., Kohler, M., Sutiwaraphun, J., To, H.W., Yang, D.: Large scale data mining: Challenges and responses. In Pregibon, D., Uthurusamy, R., eds.: Proceedings Third International Conference on Knowledge Discovery and Data Mining, Newport Beach, CA, AAAI Press (1997) 61-64

....This approach is related in nature to the partitioned tree construction approach discussed in this paper. In the partitioned tree construction approach, actual data samples are partitioned (horizontal partitioning) whereas in this approach attributes are partitioned (vertical partitioning) 6 In [8], a few general approaches for parallelizing C4.5 are discussed. In the Dynamic Task Distribution (DTD) scheme, a master processor allocates a subtree of the decision tree to an idle slave processor. This scheme does not require communication among processors, but suffers from the load imbalance. ....

....this paper and suffers from the high communication overhead. The DP att scheme distributes the attributes. This scheme has the advantages of being both load balanced and requiring minimal communications. However, this scheme does not scale well with increasing number of processors. The results in [8] show that the effectiveness of different parallelization schemes varies significantly with data sets being used. Kufrin proposed an approach called Parallel Decision Trees (PDT) in [15] This approach is similar to the DP rec scheme [8] and synchronous tree construction approach discussed in ....

[Article contains additional citation context not shown here]

J. Chattratichat, J. Darlington, M. Ghanem, Y. Guo, H. Huning, M. Kohler, J. Sutiwaraphun, H.W. To, and D. Yang. Large scale data mining: Challenges and responses. In Proc. of the Third Int'l Conference on Knowledge Discovery and Data Mining, 1997.

....slower, it is appealing to employ parallel computing and to exploit the main memory of all the processors. Parallel data mining algorithms have been recently considered for tasks such as association rules and classification, see, for example, Agrawal and Shafer [1] Chattratichat et al. [6], Cheung and Xiao [8] Han, Karypis, and Kumar [22] Joshi, Karypis, and Kumar [24] Kargupta, Hamzaoglu, and Stafford [25] Shafer, Agrawal, and Mehta [32] Srivastava, et al. 38] Zaki, Ho, and Agrawal [41] and Zaki et al. 42] Also, see Stolorz and Musick [39] and Freitas and Lavington [17] ....

J. Chattratichat, J. Darlington, M. Ghanem, Y. Guo, H. Huning, M. Kohler, J. Sutiwaraphun, H. W. To, and D. Yang. Large scale data mining: Challenges and responses. In D. Pregibon and R. Uthurusamy, editors, Proceedings Third International Conference on Knowledge Discovery and Data Mining, Newport Beach, CA, pages 61--64. AAAI Press, 1997.

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J. Chattratichat, etc., Large scale data mining: challenges and responses. In: Proceedings of International Conference on Knowledge Discovery and Data Mining, 1997: 143-146.

No context found.

J. Chattratichat, et al., Large scale data mining: challenges and responses. In: Proceedings of Proceedings of the Third International Conference on Knowledge Discovery and Data Mining, (KDD-97), Newport Beach, California, USA, AAAI Press, August 14-17, 1997: 143-146.

No context found.

J. Chattratichat, et al., Large scale data mining: challenges and responses. In: Proceedings of Proceedings of the Third International Conference on Knowledge Discovery and Data Mining, (KDD-97), Newport Beach, California, USA, AAAI Press, August 14-17, 1997: 143-146.

No context found.

J. Chattratichat, etc., Large scale data mining: challenges and responses. In: Proceedings of International Conference on Knowledge Discovery and Data Mining, 1997: 143-146.

No context found.

J. Chattratichat, J. Darlington, M. Ghanem, Y. Guo, H. Huning, M. Kohler, J. Sutiwaraphun, H.W. To, and D. Yang. Large scale data mining: Challenges and responses. In Proc. of the Third Int'l Conference on Knowledge Discovery and Data Mining, 1997.

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