RapidMiner : 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.

Format:

Book

Contributor:

Hofmann, Markus (Computer scientist), editor.

Klinkenberg, Ralf, editor.

Series:

Chapman & Hall/CRC Data Mining and Knowledge Discovery Series

Language:

English

Subjects (All):

RapidMiner (Electronic resource).

Data mining.

Physical Description:

1 online resource (518 p.)

Edition:

1st edition

Place of Publication:

Boca Raton : CRC Press, [2014]

Language Note:

English

System Details:

text file

Summary:

RapidMiner is one of the most widely used open source data mining solutions world-wide. This book provides an application use case-based introduction to data mining and to RapidMiner (and RapidAnalytics.) It presents many different applications of data mining and how to implement them with RapidMiner, and it allows readers to get started with their own data mining applications with RapidMiner, or other similar tools. The software, the data sets, and RapidMiner data mining processes used and discussed in the book are made available to readers-- Provided by publisher.

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).

ISBN:

9780429171093

0429171099

9781482205497

1482205491

OCLC:

862828364