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Abstract: Classification for very large datasets has many practical applications in data mining. Techniques such as discretization and dataset sampling can be used to scale up decision tree classi ers to large datasets. Unfortunately, both of these techniques can cause a signi cant loss in accuracy. We present a novel decision tree classi er called CLOUDS, which samples the splitting points for numeric attributes followed by an estimation step to narrow the search space of the best split. CLOUDS reduces... (Update)
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...directly from this both approaches. Other approaches consider approximation techniques for scaling up the classification, e.g. sampling [1] and discretization, as well as permitting the user to specify constraints on tree size [7] Particularly, approximation techniques...
...classifiers. In Section 1.1. we introduce and compare several state of the art classification algorithms, including SPRINT[17] and CLOUDS[14]. We present our CMP family classifiers in detail in Section 2. including the data structure, techniques to avoid accuracy loss, the...
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8: SPRINT: A scalable parallel classifier for data mining - Shafer, Agrawal et al. - 1996
7: SLIQ: A fast scalable classifier for data mining - Mehta, Agrawal et al. - 1996
5: Programs for machine learning (context) - Quinlan - 1993
BibTeX entry: (Update)
K. Alsabti, S. Ranka, and V. Singh. CLOUDS: A decision tree classifier for large datasets. In 4th Intl. Conf. on Knowledge Discovery and Data Mining, Aug 1998. http://citeseer.ist.psu.edu/alsabti98clouds.html More
@inproceedings{ alsabti98clouds,
author = "Khaled Alsabti and Sanjay Ranka and Vineet Singh",
title = "{CLOUDS}: A Decision Tree Classifier for Large Datasets",
booktitle = "Knowledge Discovery and Data Mining",
pages = "2-8",
year = "1998",
url = "citeseer.ist.psu.edu/alsabti98clouds.html" }
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The graph only includes citing articles where the year of publication is known.
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