Data Mining: Practical Machine Learning Tools and Techniques, Second Edition

Now that we’ve seen how the inputs and outputs can be represented, it’s time

to look at the learning algorithms themselves. This chapter explains the basic

ideas behind the techniques that are used in practical data mining. We will not

delve too deeply into the trickier issues—advanced versions of the algorithms,

optimizations that are possible, complications that arise in practice. These topics

are deferred to Chapter 6, where we come to grips with real implementations

of machine learning methods such as the ones included in data mining toolkits

and used for real-world applications. It is important to understand these more

advanced issues so that you know what is really going on when you analyze a

particular dataset.

In this chapter we look at the basic ideas. One of the most instructive lessons

is that simple ideas often work very well, and we strongly recommend the adop-

tion of a “simplicity-first” methodology when analyzing practical datasets. There

are many different kinds of simple structure that datasets can exhibit. In one

dataset, there might be a single attribute that does all the work and the others

may be irrelevant or redundant. In another dataset, the attributes might

chapter 4

Algorithms:

The Basic Methods

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