Chapter 1
Data Mining
In this intoductory chapter we begin with the essence of data mining and a dis-
cussion of how data mining is treated by the various disciplines that contribute
to this ﬁeld. We cover “Bonferroni’s Principle,” which is really a warning about
overusing the ability to mine data. This chapter is also the place where we
summarize a few useful ideas that are not data mining per se, but are use-
ful in understanding some important data-mining concepts. These include the
TF.IDF measure of word importance, behavior of hash functions and indexes,
and identities involving e, the base of natural logarithms. Finally, we give an
outline of the topics covered in the balance of the book.
1.1
What is Data Mining?
In the 1990’s “data mining” was an exciting and popular new concept. Around
2010, people instead started to speak of “big data.” Today, the popular term is
“data science.” However, during all this time, the concept remained the same:
use the most powerful hardware, the most powerful programming systems, and
the most eﬃcient algorithms to solve problems in science, commerce, healthcare,
government, the humanities, and many other ﬁelds of human endeavor.
1.1.1
Modeling
To many, data mining is the process of creating a model from data, often by
the process of machine learning, which we mention in Section 1.1.3 and discuss
more fully in Chapter 12. However, more generally, the objective of data mining
is an algorithm. For instance, we discuss locality-sensitive hashing in Chapter 3
and a number of stream-mining algorithms in Chapter 4, none of which involve
a model. Yet in many important applications, the hard part is creating the
model, and once the model is available, the algorithm to use the model is
straightforward.
1
