RapidMiner Data Mining Use Cases and Business Analytics Applications

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 MierswaGetting Used to RapidMiner, Ingo Mierswa Basic Classification Use Cases for Credit Approval and in Education k-Nearest Neighbor Classification I, M. Fareed Akhtark-Nearest Neighbor Classification II, M. Fareed AkhtarNaïve Bayes Classification I, M. Fareed AkhtarNaïve Bayes Classification II, M. Fareed Akhtar Marketing, Cross-Selling, and Recommender System Use Cases Who Wants My Product? Affinity-Based Marketing, Euler TimmBasic Association Rule Mining in RapidMiner, Matthew A. NorthConstructing Recommender Systems in RapidMiner, Matej Mihelcic, Matko Bošnjak, Nino Antulov-Fantulin, and Tomislav ŠmucRecommender System for Selection of the Right Study Program for Higher Education Students, Milan Vukicevic, Miloš Jovanovic, Boris Delibašic, and Milija Suknovic Clustering in Medical and Educational DomainsVisualizing Clustering Validity Measures, Andrew Chisholm Text Mining: Spam Detection, Language Detection, and Customer Feedback Analysis Detecting Text Message Spam, Neil McGuiganRobust Language Identification with RapidMiner: A Text Mining Use Case, Matko Bošnjak, Eduarda Mendes Rodrigues, and Luis SarmentoText 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 RhodeMedical 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 KornhuberChemoinformatics: 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 ConstructionInstance Selection in RapidMiner, Marcin Blachnik and Miroslaw KordosAnomaly Detection, Markus Goldstein Meta-Learning, Automated Learner Selection, Feature Selection, and Parameter Optimization Using RapidMiner for Research: Experimental Evaluation of Learners, Miloš Jovanovic, Milan Vukicevic, Boris Delibašic, and Milija Suknovic Index

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.