Book Image

Mastering Data Mining with Python - Find patterns hidden in your data

By : Megan Squire
Book Image

Mastering Data Mining with Python - Find patterns hidden in your data

By: Megan Squire

Overview of this book

Data mining is an integral part of the data science pipeline. It is the foundation of any successful data-driven strategy – without it, you'll never be able to uncover truly transformative insights. Since data is vital to just about every modern organization, it is worth taking the next step to unlock even greater value and more meaningful understanding. If you already know the fundamentals of data mining with Python, you are now ready to experiment with more interesting, advanced data analytics techniques using Python's easy-to-use interface and extensive range of libraries. In this book, you'll go deeper into many often overlooked areas of data mining, including association rule mining, entity matching, network mining, sentiment analysis, named entity recognition, text summarization, topic modeling, and anomaly detection. For each data mining technique, we'll review the state-of-the-art and current best practices before comparing a wide variety of strategies for solving each problem. We will then implement example solutions using real-world data from the domain of software engineering, and we will spend time learning how to understand and interpret the results we get. By the end of this book, you will have solid experience implementing some of the most interesting and relevant data mining techniques available today, and you will have achieved a greater fluency in the important field of Python data analytics.
Table of Contents (16 chapters)
Mastering Data Mining with Python – Find patterns hidden in your data
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
Index

Chapter 2. Association Rule Mining

In our data mining toolbox, measuring the frequency of a pattern is a critical task. In some cases, more frequently occurring patterns may end up being more important patterns. If we can find frequently occurring pairs of items, or triples of items, those may be even more interesting.

In this chapter, we begin our exploration of frequent itemsets, and then we extend those to a type of pattern called association rules. We will cover the following topics:

  • What is a frequent itemset? What are the techniques for finding frequent itemsets? Where are the bottlenecks and how can we speed up the process?

  • How can we extend a frequent itemset to become an association rule?

  • What makes a good association rule? We will learn to describe the value of a particular association rule, given its level of support in the database, our confidence in the rule itself, and the value added by the rule we found.

To do this, we will write a program to find frequent itemsets in an open...