RapidMiner Data Mining Use Cases and Business Analytics Applications 1st Edition by Markus Hofmann, Ralf Klinkenberg ISBN 1482205505 9781482205503

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ISBN 10: 1482205505

ISBN 13: 9781482205503 

Author: Markus Hofmann, Ralf Klinkenberg 

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.

Table of contents:

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

Index

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Tags: Markus Hofmann, Ralf Klinkenberg, RapidMiner, Data, Mining, Use Cases, Business, Analytics, Applications