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Summary: Efficient Decision Tree Construction on Streaming Data
Ruoming Jin
Department of Computer and Information
Sciences
Ohio State University, Columbus OH 43210
jinr@cis.ohio-state.edu
Gagan Agrawal
Department of Computer and Information
Sciences
Ohio State University, Columbus OH 43210
agrawal@cis.ohio-state.edu
ABSTRACT
Decision tree construction is a well studied problem in data min-
ing. Recently, there has been much interest in mining streaming
data. Domingos and Hulten have presented a one-pass algorithm
for decision tree construction. Their work uses Hoeffding inequal-
ity to achieve a probabilistic bound on the accuracy of the tree con-
structed.
In this paper, we revisit this problem. We make the following two
contributions: 1) We present a numerical interval pruning (NIP) ap-
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