Big Data

Download this chapter on data mining

Download this excerpt from <i>Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations</i>, published by Morgan-Kauffmann, for a look at strategies for closing the gap between generating information and understanding it.


Data mining, an increasingly important business IT function, is more than simply querying databases. It’s about solving problems and finding patterns, according to Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations, by Ian H. Witten and Eibe Frank.



To effectively manipulate data, you need to understand machine learning concepts and how those concepts apply to data extraction, according to this excerpt from Morgan-Kaufmann publishers. If you’re an IT manager or developer whose job requires mining large collections of data, this book can help you “…close the growing gap between the generation of data and the understanding of it.”

This book will interest both the expert and the novice and features practical examples of how to effectively harness machine learning techniques and strategies.

Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations also includes a Java class library that you can adapt for your own data mining.

For a preview, download Chapter 1, “What’s it all about?”, which appears through special arrangement with Morgan-Kaufmann, publishers of technical information resources for computer and engineering professionals. This chapter introduces you to structural patterns, machine learning and statistics, fielded applications, bias issues, and data mining ethics.

Witten is professor of computer science at the University of Waikato in New Zealand. Frank also works at the University of Waikato as a researcher in the Machine Learning group.

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