Henrique Andrade, Tahsin Kurc, Alan Sussman, Joel Saltz  Home/Search
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Abstract: Classification of very large datasets is a challenging problem in data mining. It is desirable to have decision-tree classifiers that can handle large datasets, because a large dataset often increases the accuracy of the resulting classification model. Classification tree algorithms can benefit from parallelization because of large memory and computation requirements for handling large datasets. Clusters of shared-memory multiprocessors (SMPs), in which each shared-memory node has a small... (Update)
Context of citations to this paper: More
.... visualization and processing of digitized microscopy images [3] visualization of largescale data [5,8,29,42,61] and data mining [4,7,34,68]. Although the datasets used for analysis and the data products generated by applications that manipulate those datasets may differ...
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BibTeX entry: (Update)
H. Andrade, T. Kurc, A. Sussman, and J. Saltz. Decision tree construction for data mining on clusters of shared-memory multiprocessors. Technical Report CS-TR-4203 and UMIACS-TR- http://citeseer.ist.psu.edu/388245.html More
@misc{ andrade-decision,
author = "H. Andrade and T. Kurc and A. Sussman and J. Saltz",
title = "Decision tree construction for data mining on clusters of shared-memory
multiprocessors",
text = "H. Andrade, T. Kurc, A. Sussman, and J. Saltz. Decision tree construction
for data mining on clusters of shared-memory multiprocessors. Technical
Report CS-TR-4203 and UMIACS-TR-",
url = "citeseer.ist.psu.edu/388245.html" }
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