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Data Mining and Business Analytics with R

eBook
WILEY
ISBN/EAN: 9781118572153
Umbreit-Nr.: 4512562

Sprache: Englisch
Umfang: 368 S., 10.97 MB
Format in cm:
Einband: Keine Angabe

Erschienen am 28.05.2013
Auflage: 1/2013


E-Book
Format: EPUB
DRM: Adobe DRM
€ 103,99
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  • Zusatztext
    • <p>Collecting, analyzing, and extracting valuable information from a large amount of data requires easily accessible, robust, computational and analytical tools.<i>Data Mining and Business Analytics with R</i> utilizes the open source software R for the analysis, exploration, and simplification of large high-dimensional data sets. As a result, readers are provided with the needed guidance to model and interpret complicated data and become adept at building powerful models for prediction and classification.</p><p>Highlighting both underlying concepts and practical computational skills,<i>Data Mining and Business Analytics with R</i> begins with coverage of standard linear regression and the importance of parsimony in statistical modeling. The book includes important topics such as penalty-based variable selection (LASSO); logistic regression; regression and classification trees; clustering; principal components and partial least squares; and the analysis of text and network data. In addition, the book presents:</p><ul><li>A thorough discussion and extensive demonstration of the theory behind the most useful data mining tools</li><li>Illustrations of how to use the outlined concepts in real-world situations</li><li>Readily available additional data sets and related R code allowing readers to apply their own analyses to the discussed materials</li><li>Numerous exercises to help readers with computing skills and deepen their understanding of the material</li></ul><p><i>Data Mining and Business Analytics with R</i> is an excellent graduate-level textbook for courses on data mining and business analytics. The book is also a valuable reference for practitioners who collect and analyze data in the fields of finance, operations management, marketing, and the information sciences.</p>
  • Kurztext
    • Inhaltsangabe<p>Preface</p><p>Acknowledgements</p><p><b>1. Introduction</b></p><p>Reference</p><p><b>2. Processing the Information and Getting to Know Your Data</b></p><p>2.1 Example 1: 2006 Birth Data</p><p>2.2 Example 2: Alumni Donations</p><p>2.3 Example 3: Orange Juice</p><p>References</p><p><b>3. Standard Linear Regression</b></p><p>3.1 Example 1: Fuel Efficiency of Automobiles</p><p>3.2 Example 2: Toyota Used Car Prices</p><p>Appendix: The Effects of Model Over fitting on the Average Mean Square Error of the Regression Prediction</p><p>References</p><p><b>4. Local Polynomial Regression: A Nonparametric Regression Approach</b></p><p>4.1 Example 1: Old Faithful</p><p>4.2 Example 2: NOx Exhaust Emissions</p><p>References</p><p><b>5. Importance of Parsimony in Statistical Modeling</b></p><p>References</p><p><b>6. Penalty-Based Variable Selection in Regression Models with Many</b> <b>Parameters (LASSO)</b></p><p>6.1 Example 1: Prostate Cancer</p><p>6.2 Example 2: Orange Juice References</p><p><b>7. Logistic Regression</b></p><p>7.1 Example 1: Death Penalty Data</p><p>7.2 Example 2: Delayed Airplanes</p><p>7.3 Example 3: Loan Acceptance</p><p>7.4 Example 4: German Credit Data</p><p>References</p><p><b>8. Binary Classification, Probabilities and Evaluating Classification Performance</b></p><p>8.1 Example: German Credit Data</p><p>References</p><p><b>9. Classification Using a Nearest Neighbor Analysis</b></p><p>9.1 Example 1: Forensic Glass</p><p>9.2 Example 2: German Credit Data</p><p><b>10. The Na&iuml;ve Bayesian Analysis: A Model for Predicting a Categorical Response from Mostly Categorical Predictor Variables</b></p><p>10.1 Example: Delayed Airplanes</p><p>Reference</p><p>11. Multinomial Logistic Regression</p><p>11.1 Example 1: Forensic Glass</p><p>11.2 Example 2: Forensic Glass Revisited</p><p>Appendix: Specification of a Simple Triplet Matrix</p><p>References</p><p><b>12. More on Classification and a Discussion of Discriminant Analysis</b> 12.1 Example 1: German Credit Data</p><p>12.2 Example 2: Fisher Iris Data</p><p>12.3 Example 3: Forensic Glass Data</p><p>12.4 Example 4: MBA Admission Data</p><p>Reference</p><p><b>13. Decision Trees</b></p><p>13.1Example 1: Prostate Cancer</p><p>13.2 Example 2: Motorcycle Acceleration</p><p>13.3 Example 3: Fisher Iris Data Revisited</p><p><b>14. Further Discussion on Regression and Classification Trees, Computer Software, and Other Useful Classification Methods</b></p><p>14.1 R Packages for Tree Construction</p><p>14.2 CHAID</p><p>14.3Ensemble Methods: Bagging, Boosting, and Random Forests</p><p>14.4Support Vector Machines (SVM)</p><p>14.5Neural Networks</p><p>14.6The R Package rattle: A Useful Graphical User Interface for Data Mining</p><p>References</p><p><b>15. Clustering</b></p><p>15.1 k-means Clustering</p><p>15.2Another Way to Look at Clustering: Applying the Expectation Maximization (EM) Algorithm to Mixtures of Normal Distributions</p><p>15.3 Hierarchical Clustering Procedures</p><p>References</p><p><b>16. Market Basket Analysis: Association Rules and Lift</b></p><p>16.1 Example 1: Estimate of &ldquo;Slant&rdquo; and Partial Least Squares</p><p>References</p><p><b>20. Analysis of Network Data</b></p><p>20.1 Example 1: Marriage and Power in 15<sup>th</sup> Century Florence</p><p>20.2 Example 2: Connections in a Friendship Network</p><p>References</p><p>Appendix: Exercises</p><p>Exercises 1</p><p>Exercises 2</p><p>Exercises 3</p><p>Exercises 4</p><p>Exercise 5</p><p>Exercise 6</p><p>Exercises 7</p><p>Appendix: References</p><p>Index</p>
  • Autorenportrait
    • <p><b>JOHANNES LEDOLTER,</b> PhD, is Professor in both the Department of Management Sciences and the Department of Statistics and Actuarial Science at the University of Iowa. He is a Fellow of the American Statistical Association and the American Society for Quality, and an Elected Member of the International Statistical Institute. Dr. Ledolter is the coauthor of<i>Statistical Methods for Forecasting, Achieving Quality Through Continual Improvement,</i> and<i>Statistical Quality Control: Strategies and Tools for Continual Improvement,</i> all published by Wiley.</p>