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Abstract: Classification is an important data mining problem. Although classification is a wellstudied problem, most of the current classification algorithms require that all or a portion of the the entire dataset remain permanently in memory. This limits their suitability for mining over large databases. We present a new decision-tree-based classification algorithm, called SPRINT that removes all of the memory restrictions, and is fast and scalable. The algorithm has also been designed to be easily... (Update)
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BibTeX entry: (Update)
J. Shafer, R. Agrawal, M. Mehta. SPRINT: A scalable parallel classifier for data mining. In 22nd VLDB Conference, Sept 1996. http://citeseer.ist.psu.edu/article/shafer96sprint.html More
@inproceedings{ shafer96sprint,
author = "John C. Shafer and Rakesh Agrawal and Manish Mehta",
title = "{SPRINT}: {A} Scalable Parallel Classifier for Data Mining",
booktitle = "Proc. 22nd Int. Conf. Very Large Databases, {VLDB}",
month = "3--6~",
publisher = "Morgan Kaufmann",
editor = "T. M. Vijayaraman and Alejandro P. Buchmann and C. Mohan and Nandlal L. Sarda",
isbn = "1-55860-382-4",
pages = "544--555",
year = "1996",
url = "citeseer.ist.psu.edu/article/shafer96sprint.html" }
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145 SPRINT: A scalable parallel classifier for data mining - Shafer, Agrawal et al. - 1996
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