Franklin

Rapid miner : data mining use cases and business analytics applications / edited by Markus Hofmann, Institute of Technology, Blanchardstown, Dublin, Ireland, Ralf Klinkenberg, Rapid-I/RapidMiner, Dortmund, Germany.

Author/Creator:
Hofmann, Markus, author.
Edition:
1st edition
Publication:
Boca Raton : CRC Press, [2014]
Series:
Chapman & Hall/CRC Data Mining and Knowledge Discovery Series
Chapman & Hall/CRC Data Mining and Knowledge Discovery Series
Format/Description:
Book
1 online resource (518 p.)
Subjects:
Data mining.
RapidMiner (Electronic resource)
Form/Genre:
Electronic books.
Language:
English
System Details:
text file
Summary:
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 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
Contents:
Front Cover; Part I Introduction to Data Mining and RapidMiner; Chapter 1 What This Book is About and What It is Not; Chapter 2 Getting Used to RapidMiner; Part 2 Basic Classification Use Cases for Credit Approval and in Education; Chapter 3 k-Nearest Neighbor Classification I; Chapter 4 k-Nearest Neighbor Classification II; Chapter 5 NaŁ ve Bayes Classification I; Chapter 6 NaŁ ve Bayes Classificaton II; Part 3 Marketing, Cross- Selling, and Recommender System Use Cases; Chapter 7 Who Wants My Product? Affnity-Based Marketing; Chapter 8 Basic Association Rule Mining in RapidMiner
Chapter 9 Constructing Recommender Systems in RapidMinerChapter 10 Recommender System for Selection of the Right Study Program for Higher Education Students; Part 4 Clustering in Medical and Educational Domains; Chapter 11 Visualising Clustering Validity Measures; Chapter 12 Grouping Higher Education Students with RapidMiner; Part 5 Text Mining: Spam Detection, Language Detection, and Customer Feedback Analysis; Chapter 13 Detecting Text Message Spam; Chapter 14 Robust Language Identification with RapidMiner: A Text Mining Use Case; Chapter 15 Text Mining with RapidMiner
Part 6 Feature Selection and Classification in Astroparticle Physics and in Medical DomainsChapter 16 Application of RapidMiner in Neutrino Astronomy; Chapter 17 Medical Data Mining; Part 7 Molecular Structure- and Property- Activity Relationship Modeling in Biochemistry and Medicine; Chapter 18 Using PaDEL to Calculate Molecular Properties and Chemoinformatic Models; Chapter 19 Chemoinformatics: Structure- and Property-activity Relationship Development; Part 8 Image Mining: Feature Extraction, Segmentation, and Classification; Chapter 20 Image Mining Extension for RapidMiner (Introductory)
Chapter 21 Image Mining Extension for RapidMiner (Advanced)Part 9 Anomaly Detection, Instance Selection, and Prototype Construction; Chapter 22 Instance Selection in RapidMiner; Chapter 23 Anomaly Detection; Part 10 Meta- Learning, Automated Learner Selection, Feature Selection, and Parameter Optimization; Chapter 24 Using RapidMiner for Research: Experimental Evaluation of Learners; Color Insert; Back Cover
Notes:
Description based upon print version of record.
Includes bibliographical references.
Description based on online resource; title from PDF title page (ebrary, viewed December 2, 2013).
Contributor:
Hofmann, Markus.
Klinkenberg, Ralf.
ISBN:
0-429-17109-9
1-4822-0549-1
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