Method for finding a best test for a nominal attribute for generating a binary decision tree
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United States of America Patent
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Aug 8, 2000
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N/A
app pub date -
Jan 26, 1998
filing date -
Jan 26, 1998
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Expired
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Abstract
A fast way for determining the best subset test for a nominal attribute in a decision tree. When a nominal attribute has n distinct values, the prior art requires computing the impurity functions on each of the 2.sup.n-1 -1 possible subset partitioning of the n values and finding the minimum case among them. This invention guarantees the minimum impurity test on the attribute by computing only (n-1) impurity function computations. This reduction of computational complexity makes it practically possible to find the true best tests for many real data mining application, where a binary decision tree is used as the classification model.
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