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Chapter 9. Moving on > 9.1. Applying data mining

9.1 Applying data mining

In 2006 a poll was taken by the organizers of the International Data Mining Conference to identify the top 10 data mining algorithms. Table 9.1 shows the results, in order. It is good to see that they are all covered in this book! The conference organizers divided the algorithms into rough categories, which are also shown. Many of the assignments are rather arbitrary—Naïve Bayes, for example, is certainly a statistical learning method, and we have introduced EM as a statistically based clustering algorithm. Nevertheless, the emphasis on classification over other forms of learning, which reflects the emphasis in this book, is evident in the table, as is the dominance of C4.5, which we have also noted. One algorithm in Table 9.1 that has not been mentioned so far is the PageRank algorithm for link mining, which we were a little surprised to see in this list. Section 9.6 contains a brief description.


  

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