Classification by decision tree induction in data mining

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If tuples in D are all of the same class, C then 3.

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November 27, 2014 Data Mining: Concepts and Techniques 7 Algorithm for Decision Tree Induction: Method 1. The Following is the sequential learning Algorithm where rules are learned for one class at a time.

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therefore w jk(new) = w jk(old) + Δ w jk.

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Classification—a classical problem extensively studied by statisticians and machine learning researchers • Scalability: Classifying data sets with millions of examples and hundreds of attributes with reasonable speed • Why decision tree induction in data mining? o Relatively faster learning speed (than other classification methods). Jan 1, 2012 · Scalability and efficiency is the major problem for classification algorithms in data mining, for large databases. Each answer then. This paper concerns the evolutionary induction of decision trees (DT) for large-scale data. . . This process of top-down induction of decision trees (TDIDT) is an example of a greedy algorithm, and it is by far the most common strategy for learning decision trees from data.

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