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[ACM Press the ninth ACM SIGKDD international conference - Washington, D.C. (2003.08.24-2003.08.27)] Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '03 - Efficient decision tree construction on streaming data

โœ Scribed by Jin, Rouming; Agrawal, Gagan

Book ID
126219337
Publisher
ACM Press
Year
2003
Tongue
English
Weight
161 KB
Category
Article
ISBN-13
9781581137378

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โœฆ Synopsis

Decision tree construction is a well studied problem in data mining. 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 inequality to achieve a probabilistic bound on the accuracy of the tree constructed.In this paper, we revisit this problem. We make the following two contributions: 1) We present a numerical interval pruning (NIP) approach for efficiently processing numerical attributes. Our results show an average of 39% reduction in execution times. 2) We exploit the properties of the gain function entropy (and gini) to reduce the sample size required for obtaining a given bound on the accuracy. Our experimental results show a 37% reduction in the number of data instances required.

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