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Data Mining, 4th Edition by Christopher J. Pal, Mark A. Hall, Eibe Frank, Ian H. Witten

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Chapter 4

Algorithms

The basic methods

Abstracts

Now we plunge into the world of actual machine learning algorithms. This chapter only considers basic, principled, versions of learning algorithms, leaving advanced features that are necessary for real-world deployment for later. A rudimentary rule learning algorithm simply picks a single attribute to make predictions; the well-known “Naïve Bayes” method for probabilistic classification uses all the attributes instead, equally weighted. Next we discuss the standard “divide-and-conquer” algorithm for learning decision trees, and the “separate-and-conquer” algorithm for learning decision rules. Then we show how to efficiently mine a dataset for association rules: the seminal Apriori algorithm. Linear ...

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