Data Mining - Mining of Massive Datasets - page 2

parameters of this Gaussian. The mean and standard deviation of this Gaussian distribution
completely characterize the distribution and would become the model of the data. □

1.1.2 Machine Learning

There are some who regard data mining as synonymous with machine learning. There is
no question that some data mining appropriately uses algorithms from machine learning.
Machine-learning practitioners use the data as a training set, to train an algorithm of one of
the many types used by machine-learning practitioners, such as Bayes nets, support-vector
machines, decision trees, hidden Markov models, and many others.
There are situations where using data in this way makes sense. The typical case where
machine learning is a good approach is when we have little idea of what we are looking
for in the data. For example, it is rather unclear what it is about movies that makes certain
movie-goers like or dislike it. Thus, in answering the “Netflix challenge” to devise an al-
gorithm that predicts the ratings of movies by users, based on a sample of their responses,
machine-learning algorithms have proved quite successful. We shall discuss a simple form
of this type of algorithm in Section 9.4 .
On the other hand, machine learning has not proved successful in situations where we
can describe the goals of the mining more directly. An interesting case in point is the at-
tempt by WhizBang! Labs 1 to use machine learning to locate people's resumes on the Web.
It was not able to do better than algorithms designed by hand to look for some of the obvi-
ous words and phrases that appear in the typical resume. Since everyone who has looked at
or written a resume has a pretty good idea of what resumes contain, there was no mystery
about what makes a Web page a resume. Thus, there was no advantage to machine-learning
over the direct design of an algorithm to discover resumes.

1.1.3 Computational Approaches to Modeling

More recently, computer scientists have looked at data mining as an algorithmic problem.
In this case, the model of the data is simply the answer to a complex query about it. For
instance, given the set of numbers of Example 1.1 , we might compute their average and
standard deviation. Note that these values might not be the parameters of the Gaussian that
best fits the data, although they will almost certainly be very close if the size of the data is
large.
There are many different approaches to modeling data. We have already mentioned the
possibility of constructing a statistical process whereby the data could have been generated.
Most other approaches to modeling can be described as either