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.
Introduction to Data Mining and RapidMiner
What This Book Is about and What It Is Not, Ingo Mierswa
Getting Used to RapidMiner, Ingo Mierswa
Basic Classification Use Cases for Credit Approval and in Education
k-Nearest Neighbor Classification I, M. Fareed Akhtar
k-Nearest Neighbor Classification II, M. Fareed Akhtar
Naïve Bayes Classification I, M. Fareed Akhtar
Naïve Bayes Classification II, M. Fareed Akhtar
Marketing, Cross-Selling, and Recommender System Use Cases
Who Wants My Product? Affinity-Based Marketing, Euler Timm
Basic Association Rule Mining in RapidMiner, Matthew A. North
Constructing Recommender Systems in RapidMiner, Matej Mihelčić, Matko Bošnjak, Nino Antulov-Fantulin, and Tomislav Šmuc
Recommender System for Selection of the Right Study Program for Higher Education Students, Milan Vukićević, Miloš Jovanović, Boris Delibašić, and Milija Suknović
Clustering in Medical and Educational Domains
Visualizing Clustering Validity Measures, Andrew Chisholm
Text Mining: Spam Detection, Language Detection, and Customer Feedback Analysis
Detecting Text Message Spam, Neil McGuigan
Robust Language Identification with RapidMiner: A Text Mining Use Case, Matko Bošnjak, Eduarda Mendes Rodrigues, and Luis Sarmento
Text Mining with RapidMiner, Gurdal Ertek, Dilek Tapucu, and Inanc Arin
Feature Selection and Classification in Astroparticle Physics and in Medical Domains
Application of RapidMiner in Neutrino Astronomy, Tim Ruhe, Katharina Morik, and Wolfgang Rhode
Medical Data Mining, Mertik Matej and Palfy Miroslav
Molecular Structure- and Property-Activity Relationship Modeling in Biochemistry and Medicine
Using PaDEL to Calculate Molecular Properties and Chemoinformatic Models, Markus Muehlbacher and Johannes Kornhuber
Chemoinformatics: Structure- and Property-Activity Relationship Development with RapidMiner, Markus Muehlbacher and Johannes Kornhuber
Image Mining: Feature Extraction, Segmentation, and Classification
Image Mining Extension for RapidMiner (Introductory), Radim Burget, Václav Uher, and Jan Masek
Image Mining Extension for RapidMiner (Advanced), Václav Uher and Radim Burget
Anomaly Detection, Instance Selection, and Prototype Construction
Instance Selection in RapidMiner, Marcin Blachnik and Miroslaw Kordos
Anomaly Detection, Markus Goldstein
Meta-Learning, Automated Learner Selection, Feature Selection, and Parameter Optimization
Using RapidMiner for Research: Experimental Evaluation of Learners, Miloš Jovanović, Milan Vukićević, Boris Delibašić, and Milija Suknović
Markus Hofmann is a lecturer at the Institute of Technology Blanchardstown, where he focuses on data mining, text mining, data exploration and visualization, and business intelligence. Dr. Hofmann is a member of the Register of Expert Panellists of the Irish Higher Education and Training Awards council, an external examiner to two other third-level institutes, and a specialist in undergraduate and postgraduate course development. He received his PhD from Trinity College Dublin.
Ralf Klinkenberg is the co-founder of Rapid-I and CBDO of Rapid-I Germany. Rapid-I is the company behind the open source software solution RapidMiner and its server version RapidAnalytics. Mr. Klinkenberg has more than 15 years of consulting and training experience in data mining and RapidMiner-based solutions. He received his MS in computer science from the Technical University of Dortmund and Missouri University of Science and Technology.
In this book, case studies communicate how to analyze databases, text collections, and image data. … How the given data are transformed to meet the requirements of the method is illustrated by screenshots of RapidMiner. The RapidMiner processes and datasets described in the case studies are published on the companion web page of this book. The inspiring applications may be used as a blueprint and a justification of future applications.
—Professor Dr. Katharina Morik, Technical University of DortmundHofmann and Klinkenberg have produced a fine collection of essays on data mining and analytic models, presented in several cross-disciplinary cases. This book describes data mining and case applications using Rapidminer models and analytic techniques. . The book represents the work of more than 30 contributors. Managing the writing styles of so many contributors is a challenging task, and the editors are to be commended for their effort. The material flows well, is very readable, and easily transitions from chapter to chapter and section to section. The book is divided into ten sections, each focusing on a different disciplinary area and a different analytic and mining model. Each section includes one or more cases. .. This is a good book. If you are interested in some very interesting data mining cases, or if you would like to learn Rapidminer, it will not disappoint. The bibliographic references are lengthy and the indices are well done.
—Book review appearing in ACM Computing Reviews , March 2014