Key Features

Dive deeper into data mining with Python – don't be complacent, sharpen your skills!

From the most common elements of data mining to cutting-edge techniques, we've got you covered for any data-related challenge

Become a more fluent and confident Python data-analyst, in full control of its extensive range of libraries

Book Description

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.

What you will learn

Explore techniques for finding frequent itemsets and association rules in large data sets

Learn identification methods for entity matches across many different types of data

Identify the basics of network mining and how to apply it to real-world data sets

Discover methods for detecting the sentiment of text and for locating named entities in text

Observe multiple techniques for automatically extracting summaries and generating topic models for text

See how to use data mining to fix data anomalies and how to use machine learning to identify outliers in a data set

About the Author

Megan Squire

is a professor of computing sciences at Elon University.

Her primary research interest is in collecting, cleaning, and analyzing data about how free and open source software is made. She is one of the leaders of the FLOSSmole.org, FLOSSdata.org, and FLOSSpapers.org projects.

Table of Contents

Expanding Your Data Mining Toolbox

Association Rule Mining

Entity Matching

Network Analysis

Sentiment Analysis in Text

Named Entity Recognition in Text

Automatic Text Summarization

Topic Modeling in Text

Mining for Data Anomalies

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