RainForest - a Framework for Fast Decision Tree Construction of Large Datasets

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RainForest - a Framework for Fast Decision Tree Construction of Large Datasets (1998)

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by Johannes Gehrke , Raghu Ramakrishnan , Venkatesh Ganti

Venue:	In VLDB
Citations:	115 - 8 self

BibTeX

@INPROCEEDINGS{Gehrke98rainforest-,
    author = {Johannes Gehrke and Raghu Ramakrishnan and Venkatesh Ganti},
    title = {RainForest - a Framework for Fast Decision Tree Construction of Large Datasets},
    booktitle = {In VLDB},
    year = {1998},
    pages = {416--427},
    publisher = {Morgan Kaufmann}
}

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Abstract

Classification of large datasets is an important data mining problem. Many classification algorithms have been proposed in the literature, but studies have shown that so far no algorithm uniformly outperforms all other algorithms in terms of quality. In this paper, we present a unifying framework for decision tree classifiers that separates the scalability aspects of algorithms for constructing a decision tree from the central features that determine the quality of the tree. This generic algorithm is easy to instantiate with specific algorithms from the literature (including C4.5, CART,

Keyphrases

large datasets fast decision tree construction decision tree classifier specific algorithm decision tree unifying framework central feature many classification algorithm important data mining problem generic algorithm scalability aspect

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