English | 2016 | ISBN: 978-1-78588-995-0 | 268 Pages | PDF, EPUB, MOBI | 12 MB
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