document: Efficient decision tree construction on streaming data 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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