Data Mining for Business Analytics: Concepts, Techniques, and Applications in R

Cover
John Wiley & Sons, 05.09.2017 - 576 Seiten

Data Mining for Business Analytics: Concepts, Techniques, and Applications in R presents an applied approach to data mining concepts and methods, using R software for illustration

Readers will learn how to implement a variety of popular data mining algorithms in R (a free and open-source software) to tackle business problems and opportunities.

This is the fifth version of this successful text, and the first using R. It covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, recommender systems, clustering, text mining and network analysis. It also includes:

  • Two new co-authors, Inbal Yahav and Casey Lichtendahl, who bring both expertise teaching business analytics courses using R, and data mining consulting experience in business and government
  • Updates and new material based on feedback from instructors teaching MBA, undergraduate, diploma and executive courses, and from their students
  • More than a dozen case studies demonstrating applications for the data mining techniques described
  • End-of-chapter exercises that help readers gauge and expand their comprehension and competency of the material presented
  • A companion website with more than two dozen data sets, and instructor materials including exercise solutions, PowerPoint slides, and case solutions www.dataminingbook.com

Data Mining for Business Analytics: Concepts, Techniques, and Applications in R is an ideal textbook for graduate and upper-undergraduate level courses in data mining, predictive analytics, and business analytics. This new edition is also an excellent reference for analysts, researchers, and practitioners working with quantitative methods in the fields of business, finance, marketing, computer science, and information technology.

 

Ausgewählte Seiten

Inhalt

Introduction
3
Overview of the Data Mining Process
15
Dimension Reduction
91
Evaluating Predictive Performance
117
kNearest Neighbors kNN
173
The Naive Bayes Classifier
187
Classification and Regression Trees
205
Logistic Regression
237
Association Rules and Collaborative Filtering
327
Cluster Analysis
355
Handling Time Series
385
RegressionBased Forecasting
399
Smoothing Methods
431
Social Network Analytics
453
Text Mining
477
Cases
497

Neural Nets
271
Discriminant Analysis
291
Ensembles and Uplift Modeling
309
References
529
Urheberrecht

Andere Ausgaben - Alle anzeigen

Data Mining for Business Analytics: Concepts, Techniques, and Applications ...
Galit Shmueli,Peter C. Bruce,Mia L. Stephens,Nitin R. Patel
Eingeschränkte Leseprobe - 2016
Data Mining for Business Analytics: Concepts, Techniques, and Applications ...
Galit Shmueli,Peter C. Bruce,Mia L. Stephens,Nitin R. Patel
Eingeschränkte Leseprobe - 2016
Data Mining for Business Analytics: Concepts, Techniques, and Applications in R
Galit Shmueli,Peter C. Bruce,Inbal Yahav,Nitin R. Patel,Kenneth C. Lichtendahl, Jr.
Eingeschränkte Leseprobe - 2017
Alle anzeigen »

Häufige Begriffe und Wortgruppen

accuracy actual algorithm analysis analytics applied approach association average binary called categorical Chapter chart choose classification clusters coefficients column combinations components compute confusion consider contains correlation costs creating cutoff data mining database dataset delayed describe distance error estimated example Figure flights forecast function given goal important Income interest learning lift linear regression loan logistic look matrix mean measure methods moving multiple nodes Nonowner Note offer outcome variable output Owner partition performance period plot popular predictive predictors probability purchase random recommendations records regression model relationship residuals ridership rules sample score seasonality selection shown shows similar single smoothing statistical step Table task training data tree trend TRUE validation set values variables visualization weights

Autoren-Profil (2017)

Galit Shmueli, PhD, is Distinguished Professor at National Tsing Hua University's Institute of Service Science. She has designed and instructed data mining courses since 2004 at University of Maryland, Statistics.com, Indian School of Business, and National Tsing Hua University, Taiwan. Professor Shmueli is known for her research and teaching in business analytics, with a focus on statistical and data mining methods in information systems and healthcare. She has authored over 70 publications including books.

Peter C. Bruce is President and Founder of the Institute for Statistics Education at Statistics.com. He has written multiple journal articles and is the developer of Resampling Stats software. He is the author of Introductory Statistics and Analytics: A Resampling Perspective (Wiley) and co-author of Practical Statistics for Data Scientists: 50 Essential Concepts (O'Reilly).

Inbal Yahav, PhD, is Professor at the Graduate School of Business Administration at Bar-Ilan University, Israel. She teaches courses in social network analysis, advanced research methods, and software quality assurance. Dr. Yahav received her PhD in Operations Research and Data Mining from the University of Maryland, College Park.

Nitin R. Patel, PhD, is Chairman and cofounder of Cytel, Inc., based in Cambridge, Massachusetts. A Fellow of the American Statistical Association, Dr. Patel has also served as a Visiting Professor at the Massachusetts Institute of Technology and at Harvard University. He is a Fellow of the Computer Society of India and was a professor at the Indian Institute of Management, Ahmedabad, for 15 years.

Kenneth C. Lichtendahl, Jr., PhD, is Associate Professor at the University of Virginia. He is the Eleanor F. and Phillip G. Rust Professor of Business Administration and teaches MBA courses in decision analysis, data analysis and optimization, and managerial quantitative analysis. He also teaches executive education courses in strategic analysis and decision-making, and managing the corporate aviation function.

Bibliografische Informationen

TitelData Mining for Business Analytics: Concepts, Techniques, and Applications in R
AutorenGalit Shmueli, Peter C. Bruce, Inbal Yahav, Nitin R. Patel, Kenneth C. Lichtendahl, Jr.
Ausgabeillustriert
VerlagJohn Wiley & Sons, 2017
ISBN1118879368, 9781118879368
Länge576 Seiten
  
Zitat exportierenBiBTeX EndNote RefMan