Deal with your data more efficiently using RapidMiner with Packt's riveting New Book and eBook!

December 2013 | Open Source

Packt is proud to present its latest publication, Exploring Data with RapidMiner by Andrew Chisholm. This book is a step-by-step tutorial style using examples so that users of different levels of how to benefit from the features offered by RapidMiner . The book is 162 pages long and is priced at $39.99 while the eBook is $20.39.

About the Author :

Andrew Chisholm completed his degree in Physics from Oxford University nearly thirty years ago. This coincided with the growth in software engineering and it led him to a career in the IT industry. For the last decade he has been very involved in mobile telecommunications, where he is currently a product manager for a market-leading test and monitoring solution used by many mobile operators worldwide. Throughout his career, he has always maintained an active interest in all aspects of data. In particular, he has always enjoyed finding ways to extract value from data and presenting this in compelling ways to help others meet their objectives. He is a certified RapidMiner expert and has been using this product to solve real problems for several years.He has published conference papers relating to unsupervised clustering and cluster validity measures and contributed a chapter called Visualizing cluster validity measures to an upcoming book entitled RapidMiner: Use Cases and Business Analytics Applications, Chapman & Hall/CRC.

RapidMiner is a software platform that provides an integrated environment for machine learning, data mining, text mining, predictive analytics and business analytics. It is written in the Java programming language. RapidMiner provides a GUI to design and execute analytical workflows.

Exploring Data with RapidMiner is a helpful guide that presents the important steps in a logical order. The book starts with importing data and then leads the readers through cleaning, handling missing values, visualizing, and extracting additional information. The book also deals with how to detect outliers and missing data and the methods to handle them. The readers will also get an understanding of the time constraints that real data places on getting a result. The book uses real examples to help the readers understand how to set up processes, quickly.

This book will give the readers a solid understanding of the possibilities that RapidMiner gives for exploring data and inspire them to use it for their own work.


Following are the chapters in the book:

Chapter 1: Setting the Scene

Chapter 2: Loading Data

Chapter 3: Visualizing Data

Chapter 4: Parsing and Converting Attributes

Chapter 5: Outliers

Chapter 6: Missing Values

Chapter 7: Transforming Data

Chapter 8: Reducing Data Size

Chapter 9: Resource Constraints

Chapter 10: Debugging

Chapter 11: Taking Stock

Exploring Data with RapidMiner
If you are a computer scientist or an engineer who has real data from which you want to extract value, this book is ideal for you. You will need to have at least a basic awareness of data mining techniques and some exposure to RapidMiner.

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