RapidMiner
Data Mining Use Cases and Business Analytics Applications
Published on 19. April 2016
525 pages
978-1-4987-5986-1 (ISBN)
Description
Powerful, Flexible Tools for a Data-Driven WorldAs 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 incre
Reviews / Votes
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
More details
Series
Language
English
Place of publication
Philadelphia, PA
United States
Publishing group
Taylor & Francis Inc
Target group
Professional and scholarly
Product notice
Reflowable
File size
9,64 MB
ISBN-13
978-1-4987-5986-1 (9781498759861)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Markus Hofmann | Ralf Klinkenberg
RapidMiner
Data Mining Use Cases and Business Analytics Applications
Book
10/2013
1st Edition
Chapman & Hall/CRC
€130.40
Article not available for order
Persons
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.
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.
Content
Introduction to Data Mining and RapidMiner. Basic Classification Use Cases for Credit Approval and in Education. Marketing, Cross-Selling, and Recommender System Use Cases. Clustering in Medical and Educational Domains. Text Mining: Spam Detection, Language Detection, and Customer Feedback Analysis. Feature Selection and Classification in Astroparticle Physics and in Medical Domains. Molecular Structure- and Property-Activity Relationship Modeling in Biochemistry and Medicine. Image Mining: Feature Extraction, Segmentation, and Classification. Anomaly Detection, Instance Selection, and Prototype Construction. Meta-Learning, Automated Learner Selection, Feature Selection, and Parameter Optimization. Index.
