RapidMiner: Data Mining Use Cases and Business Analytics Applications

Markus Hofmann, Ralf Klinkenberg

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October 25, 2013 by Chapman and Hall/CRC
Reference - 525 Pages - 19 Color & 318 B/W Illustrations
ISBN 9781482205497 - CAT# K21452
Series: Chapman & Hall/CRC Data Mining and Knowledge Discovery Series

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Features

  • Introduces the most important machine learning algorithms, data pre-processing, and transformation techniques
  • Draws on contributions from data mining experts, including the creators of the popular RapidMiner software
  • Presents examples of successful applications that can be used as blueprints for you to tackle your own data mining tasks using RapidMiner and RapidAnalytics
  • Covers numerous application areas, including retail, banking, marketing, communication, education, security, medicine, physics, and chemistry
  • Provides open source editions of the RapidMiner and RapidAnalytics software and datasets at www.RapidMiner.com

Summary

Powerful, Flexible Tools for a Data-Driven World
As the data deluge continues in today’s world, the need to master data mining, predictive analytics, and business analytics has never been greater. These techniques and tools provide unprecedented insights into data, enabling better decision making and forecasting, and ultimately the solution of increasingly complex problems.

Learn from the Creators of the RapidMiner Software
Written by leaders in the data mining community, including the developers of the RapidMiner software, RapidMiner: Data Mining Use Cases and Business Analytics Applications provides an in-depth introduction to the application of data mining and business analytics techniques and tools in scientific research, medicine, industry, commerce, and diverse other sectors. It presents the most powerful and flexible open source software solutions: RapidMiner and RapidAnalytics. The software and their extensions can be freely downloaded at www.RapidMiner.com.

Understand Each Stage of the Data Mining Process
The book and software tools cover all relevant steps of the data mining process, from data loading, transformation, integration, aggregation, and visualization to automated feature selection, automated parameter and process optimization, and integration with other tools, such as R packages or your IT infrastructure via web services. The book and software also extensively discuss the analysis of unstructured data, including text and image mining.

Easily Implement Analytics Approaches Using RapidMiner and RapidAnalytics
Each chapter describes an application, how to approach it with data mining methods, and how to implement it with RapidMiner and RapidAnalytics. These application-oriented chapters give you not only the necessary analytics to solve problems and tasks, but also reproducible, step-by-step descriptions of using RapidMiner and RapidAnalytics. The case studies serve as blueprints for your own data mining applications, enabling you to effectively solve similar problems.