Chapter 1. Expanding Your Data Mining Toolbox

When faced with sensory information, human beings naturally want to find patterns to explain, differentiate, categorize, and predict. This process of looking for patterns all around us is a fundamental human activity, and the human brain is quite good at it. With this skill, our ancient ancestors became better at hunting, gathering, cooking, and organizing. It is no wonder that pattern recognition and pattern prediction were some of the first tasks humans set out to computerize, and this desire continues in earnest today. Depending on the goals of a given project, finding patterns in data using computers nowadays involves database systems, artificial intelligence, statistics, information retrieval, computer vision, and any number of other various subfields of computer science, information systems, mathematics, or business, just to name a few. No matter what we call this activity – knowledge discovery in databases, data mining, data science – its primary mission is always to find interesting patterns.

Despite this humble-sounding mission, data mining has existed for long enough and has built up enough variation in how it is implemented that it has now become a large and complicated field to master. We can think of a cooking school, where every beginner chef is first taught how to boil water and how to use a knife before moving to more advanced skills, such as making puff pastry or deboning a raw chicken. In data mining, we also have common techniques that even the newest data miners will learn: How to build a classifier and how to find clusters in data. The title of this book, however, is Mastering Data Mining with Python, and so, as a mastering-level book, the aim is to teach you some of the techniques you may not have seen in earlier data mining projects.

In this first chapter, we will cover the following topics: