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Data Mining for Business Analytics: Concepts, Techniques, and Applications with XLMiner, 3. udgave
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Data Mining for Business Analytics: Concepts, Techniques, and Applications with XLMiner Vital Source e-bog

Galit Shmueli, Peter C. Bruce og Nitin R. Patel
(2016)
John Wiley & Sons
1.326,00 kr.
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Data Mining for Business Analytics: Concepts, Techniques, and Applications with XLMiner, 3. udgave

Data Mining for Business Analytics: Concepts, Techniques, and Applications with XLMiner Vital Source e-bog

Galit Shmueli, Peter C. Bruce og Nitin R. Patel
(2016)
John Wiley & Sons
355,00 kr.
Leveres umiddelbart efter køb
Data Mining for Business Analytics: Concepts, Techniques, and Applications with XLMiner, 3. udgave

Data Mining for Business Analytics: Concepts, Techniques, and Applications with XLMiner Vital Source e-bog

Galit Shmueli, Peter C. Bruce og Nitin R. Patel
(2016)
John Wiley & Sons
259,00 kr.
Leveres umiddelbart efter køb
Data Mining for Business Analytics - Concepts, Techniques, and Applications with XLMiner, 3. udgave

Data Mining for Business Analytics

Concepts, Techniques, and Applications with XLMiner
Galit Shmueli, Peter C. Bruce og Nitin R. Patel
(2016)
Sprog: Engelsk
John Wiley & Sons, Incorporated
1.421,00 kr.
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Detaljer om varen

  • 3. Udgave
  • Vital Source searchable e-book (Reflowable pages)
  • Udgiver: John Wiley & Sons (April 2016)
  • Forfattere: Galit Shmueli, Peter C. Bruce og Nitin R. Patel
  • ISBN: 9781118729243

Praise for the Second Edition

 

"…full of vivid and thought-provoking anecdotes... needs to be read by anyone with a serious interest in research and marketing."

- Research Magazine

 

"Shmueli et al. have done a wonderful job in presenting the field of data mining - a welcome addition to the literature."

- ComputingReviews.com

 

"Excellent choice for business analysts...The book is a perfect fit for its intended audience." 

- Keith McCormick, Consultant and Author of SPSS Statistics For Dummies, Third Edition and SPSS Statistics for Data Analysis and Visualization

 

"…extremely well organized, clearly written and introduces all of the basic ideas quite well." 

- Robert L. Phillips, Professor of Professional Practice, Columbia Business School

 

Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner®, Third Edition presents an applied approach to data mining and predictive analytics with clear exposition, hands-on exercises, and real-life case studies. Readers will work with all of the standard data mining methods using the Microsoft® Office Excel® add-in XLMiner® to develop predictive models and learn how to obtain business value from Big Data.

Featuring updated topical coverage on text mining, social network analysis, collaborative filtering, ensemble methods, uplift modeling and more, the Third Edition also includes:

  • Real-world examples to build a theoretical and practical understanding of key data mining methods 
  • End-of-chapter exercises that help readers better understand the presented material
  • Data-rich case studies to illustrate various applications of data mining techniques
  • Completely new chapters on social network analysis and text mining
  • A companion site with additional data sets, instructors material that include solutions to exercises and case studies, and Microsoft PowerPoint® slides
  • Free 140-day license to use XLMiner for Education software

Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner®, Third Edition is an ideal textbook for upper-undergraduate and graduate-level courses as well as professional programs on data mining, predictive modeling, and Big Data analytics. The new edition is also a unique reference for analysts, researchers, and practitioners working with predictive analytics in the fields of business, finance, marketing, computer science, and information technology.

Licens varighed:
Bookshelf online: 5 år fra købsdato.
Bookshelf appen: ubegrænset dage fra købsdato.

Udgiveren oplyser at følgende begrænsninger er gældende for dette produkt:
Print: 10 sider kan printes ad gangen
Copy: højest 2 sider i alt kan kopieres (copy/paste)

Detaljer om varen

  • 3. Udgave
  • Vital Source 365 day rentals (dynamic pages)
  • Udgiver: John Wiley & Sons (April 2016)
  • Forfattere: Galit Shmueli, Peter C. Bruce og Nitin R. Patel
  • ISBN: 9781118729243R365

Praise for the Second Edition

 

"…full of vivid and thought-provoking anecdotes... needs to be read by anyone with a serious interest in research and marketing."

- Research Magazine

 

"Shmueli et al. have done a wonderful job in presenting the field of data mining - a welcome addition to the literature."

- ComputingReviews.com

 

"Excellent choice for business analysts...The book is a perfect fit for its intended audience." 

- Keith McCormick, Consultant and Author of SPSS Statistics For Dummies, Third Edition and SPSS Statistics for Data Analysis and Visualization

 

"…extremely well organized, clearly written and introduces all of the basic ideas quite well." 

- Robert L. Phillips, Professor of Professional Practice, Columbia Business School

 

Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner®, Third Edition presents an applied approach to data mining and predictive analytics with clear exposition, hands-on exercises, and real-life case studies. Readers will work with all of the standard data mining methods using the Microsoft® Office Excel® add-in XLMiner® to develop predictive models and learn how to obtain business value from Big Data.

Featuring updated topical coverage on text mining, social network analysis, collaborative filtering, ensemble methods, uplift modeling and more, the Third Edition also includes:

  • Real-world examples to build a theoretical and practical understanding of key data mining methods 
  • End-of-chapter exercises that help readers better understand the presented material
  • Data-rich case studies to illustrate various applications of data mining techniques
  • Completely new chapters on social network analysis and text mining
  • A companion site with additional data sets, instructors material that include solutions to exercises and case studies, and Microsoft PowerPoint® slides
  • Free 140-day license to use XLMiner for Education software

Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner®, Third Edition is an ideal textbook for upper-undergraduate and graduate-level courses as well as professional programs on data mining, predictive modeling, and Big Data analytics. The new edition is also a unique reference for analysts, researchers, and practitioners working with predictive analytics in the fields of business, finance, marketing, computer science, and information technology.

Licens varighed:
Bookshelf online: 5 år fra købsdato.
Bookshelf appen: 5 år fra købsdato.

Udgiveren oplyser at følgende begrænsninger er gældende for dette produkt:
Print: 10 sider kan printes ad gangen
Copy: højest 2 sider i alt kan kopieres (copy/paste)

Detaljer om varen

  • 3. Udgave
  • Vital Source 120 day rentals (dynamic pages)
  • Udgiver: John Wiley & Sons (April 2016)
  • Forfattere: Galit Shmueli, Peter C. Bruce og Nitin R. Patel
  • ISBN: 9781118729243R120

Praise for the Second Edition

 

"…full of vivid and thought-provoking anecdotes... needs to be read by anyone with a serious interest in research and marketing."

- Research Magazine

 

"Shmueli et al. have done a wonderful job in presenting the field of data mining - a welcome addition to the literature."

- ComputingReviews.com

 

"Excellent choice for business analysts...The book is a perfect fit for its intended audience." 

- Keith McCormick, Consultant and Author of SPSS Statistics For Dummies, Third Edition and SPSS Statistics for Data Analysis and Visualization

 

"…extremely well organized, clearly written and introduces all of the basic ideas quite well." 

- Robert L. Phillips, Professor of Professional Practice, Columbia Business School

 

Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner®, Third Edition presents an applied approach to data mining and predictive analytics with clear exposition, hands-on exercises, and real-life case studies. Readers will work with all of the standard data mining methods using the Microsoft® Office Excel® add-in XLMiner® to develop predictive models and learn how to obtain business value from Big Data.

Featuring updated topical coverage on text mining, social network analysis, collaborative filtering, ensemble methods, uplift modeling and more, the Third Edition also includes:

  • Real-world examples to build a theoretical and practical understanding of key data mining methods 
  • End-of-chapter exercises that help readers better understand the presented material
  • Data-rich case studies to illustrate various applications of data mining techniques
  • Completely new chapters on social network analysis and text mining
  • A companion site with additional data sets, instructors material that include solutions to exercises and case studies, and Microsoft PowerPoint® slides
  • Free 140-day license to use XLMiner for Education software

Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner®, Third Edition is an ideal textbook for upper-undergraduate and graduate-level courses as well as professional programs on data mining, predictive modeling, and Big Data analytics. The new edition is also a unique reference for analysts, researchers, and practitioners working with predictive analytics in the fields of business, finance, marketing, computer science, and information technology.

Licens varighed:
Bookshelf online: 120 dage fra købsdato.
Bookshelf appen: 120 dage fra købsdato.

Udgiveren oplyser at følgende begrænsninger er gældende for dette produkt:
Print: 10 sider kan printes ad gangen
Copy: højest 2 sider i alt kan kopieres (copy/paste)

Detaljer om varen

  • 3. Udgave
  • Hardback: 560 sider
  • Udgiver: John Wiley & Sons, Incorporated (April 2016)
  • Forfattere: Galit Shmueli, Peter C. Bruce og Nitin R. Patel
  • ISBN: 9781118729274

Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner®, Third Edition presents an applied approach to data mining and predictive analytics with clear exposition, hands-on exercises, and real-life case studies. Readers will work with all of the standard data mining methods using the Microsoft® Office Excel® add-in XLMiner® to develop predictive models and learn how to obtain business value from Big Data.

Featuring updated topical coverage on text mining, social network analysis, collaborative filtering, ensemble methods, uplift modeling and more, the Third Edition also includes:

  • Real-world examples to build a theoretical and practical understanding of key data mining methods 
  • End-of-chapter exercises that help readers better understand the presented material
  • Data-rich case studies to illustrate various applications of data mining techniques
  • Completely new chapters on social network analysis and text mining
  • A companion site with additional data sets, instructors material that include solutions to exercises and case studies, and Microsoft PowerPoint® slides https://www.dataminingbook.com
  • Free 140-day license to use XLMiner for Education software

Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner®, Third Edition is an ideal textbook for upper-undergraduate and graduate-level courses as well as professional programs on data mining, predictive modeling, and Big Data analytics. The new edition is also a unique reference for analysts, researchers, and practitioners working with predictive analytics in the fields of business, finance, marketing, computer science, and information technology.

Praise for the Second Edition

"...full of vivid and thought-provoking anecdotes... needs to be read by anyone with a serious interest in research and marketing."- Research Magazine

"Shmueli et al. have done a wonderful job in presenting the field of data mining - a welcome addition to the literature." - ComputingReviews.com

"Excellent choice for business analysts...The book is a perfect fit for its intended audience."  - Keith McCormick, Consultant and Author of SPSS Statistics For Dummies, Third Edition and SPSS Statistics for Data Analysis and Visualization

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, The 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 journal articles, books, textbooks and book chapters.

Peter C. Bruce is President and Founder of the Institute for Statistics Education at www.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, also published by Wiley.

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.

Foreword xvii Preface to the Third Edition xix Preface to the First Edition xxii Acknowledgments xxiv
PART I PRELIMINARIES
CHAPTER 1 Introduction 3
1.1 What is Business Analytics? 3
1.2 What is Data Mining? 5
1.3 Data Mining and Related Terms 5
1.4 Big Data 6
1.5 Data Science 7
1.6 Why Are There So Many Different Methods? 8
1.7 Terminology and Notation 9
1.8 Road Maps to This Book 11 Order of Topics 12
CHAPTER 2 Overview of the Data Mining Process 14
2.1 Introduction 14
2.2 Core Ideas in Data Mining 15
2.3 The Steps in Data Mining 18
2.4 Preliminary Steps 20
2.5 Predictive Power and Overfitting 26
2.6 Building a Predictive Model with XLMiner 30
2.7 Using Excel for Data Mining 40
2.8 Automating Data Mining Solutions 40 Data Mining Software Tools (by Herb Edelstein) 42 Problems 45
PART II DATA EXPLORATION AND DIMENSION REDUCTION
CHAPTER 3 Data Visualization 50
3.1 Uses of Data Visualization 50
3.2 Data Examples 52 Example
1: Boston Housing Data 52 Example
2: Ridership on Amtrak Trains 53
3.3 Basic Charts: Bar Charts, Line Graphs, and Scatter Plots 53 Distribution Plots 54 Heatmaps: Visualizing Correlations and Missing Values 57
3.4 Multi-Dimensional Visualization 58 Adding Variables 59 Manipulations 61 Reference: trend line and labels 64 Scaling up to Large Datasets 65 Multivariate Plot 66 Interactive Visualization 67
3.5 Specialized Visualizations 70 Visualizing Networked Data 70 Visualizing Hierarchical Data: Treemaps 72 Visualizing Geographical Data: Map Charts 73
3.6 Summary: Major Visualizations and Operations, by Data Mining Goal 75 Prediction 75 Classification 75 Time Series Forecasting 75 Unsupervised Learning 76 Problems 77
CHAPTER 4 Dimension Reduction 79
4.1 Introduction 79
4.2 Curse of Dimensionality 80
4.3 Practical Considerations 80 Example
1: House Prices in Boston 80
4.4 Data Summaries 81
4.5 Correlation Analysis 84
4.6 Reducing the Number of Categories in Categorical Variables 85
4.7 Converting A Categorical Variable to A Numerical Variable 86
4.8 Principal Components Analysis 86 Example
2: Breakfast Cereals 87 Principal Components 92 Normalizing the Data 93 Using Principal Components for Classification and Prediction 94
4.9 Dimension Reduction Using Regression Models 96
4.10 Dimension Reduction Using Classification and Regression Trees 96 Problems 97
PART III PERFORMANCE EVALUATION
CHAPTER 5 Evaluating Predictive Performance 101
5.1 Introduction 101
5.2 Evaluating Predictive Performance 102 Benchmark: The Average 102 Prediction Accuracy Measures 103
5.3 Judging Classifier Performance 106 Benchmark: The Naive Rule 107 Class Separation 107 The Classification Matrix 107 Using the Validation Data 109 Accuracy Measures 109 Cutoff for Classification 110 Performance in Unequal Importance of Classes 114 Asymmetric Misclassification Costs 116
5.4 Judging Ranking Performance 119
5.5 Oversampling 123 Problems 129
PART IV PREDICTION AND CLASSIFICATION METHODS
CHAPTER 6 Multiple Linear Regression 134
6.1 Introduction 134
6.2 Explanatory vs. Predictive Modeling 135
6.3 Estimating the Regression Equation and Prediction 136 Example: Predicting the Price of Used Toyota Corolla Cars 137
6.4 Variable Selection in Linear Regression 141 Reducing the Number of Predictors 141 How to Reduce the Number of Predictors 142 Problems 147
CHAPTER 7 k-Nearest Neighbors (kNN) 151
7.1 The k-NN Classifier (categorical outcome) 151 Determining Neighbors 151 Classification Rule 152 Example: Riding Mowers 152 Choosing k 154 Setting the Cutoff Value 154
7.2 k-NN for a Numerical Response 156
7.3 Advantages and Shortcomings of k-NN Algorithms 158 Problems 160
CHAPTER 8 The Naive Bayes Classifier 162
8.1 Introduction 162 Example
1: Predicting Fraudulent Financial Reporting 163
8.2 Applying the Full (Exact) Bayesian Classifier 164
8.3 Advantages and Shortcomings of the Naive Bayes Classifier 172 Advantages and Shortcomings of the naive Bayes Classifier 172 Problems 176
CHAPTER 9 Classification and Regression Trees 178
9.1 Introduction 178
9.2 Classification Trees 179 Example
1: Riding Mowers 180
9.3 Measures of Impurity 183
9.4 Evaluating the Performance of a Classification Tree 187 Example
2: Acceptance of Personal Loan 188
9.5 Avoiding Overfitting 192 Stopping Tree Growth: CHAID 192 Pruning the Tree 193
9.6 Classification Rules from Trees 198
9.7 Classification Trees for More Than two Classes 198
9.8 Regression Trees 198 Prediction 199 Measuring Impurity 200 Evaluating Performance 200
9.9 Advantages and Weaknesses of a Tree 200
9.10 Improving Prediction: Multiple Trees 202 Problems 205
CHAPTER 10 Logistic Regression 209
10.1 Introduction 209
10.2 The Logistic Regression Model 211 Example: Acceptance of Personal Loan 212 Model with a Single Predictor 214 Estimating the Logistic Model from Data 215 Interpreting Results in Terms of Odds 218
10.3 Evaluating Classification Performance 219 Variable Selection 220
10.4 Example of Complete Analysis: Predicting Delayed Flights 222 Data Preprocessing 224 Model Fitting and Estimation 224 Model Interpretation 226 Model Performance 226 Variable Selection 227
10.5 Appendix: Logistic Regression for Profiling 231 Appendix A: Why Linear Regression Is Problematic for a Categorical Response 231 Appendix B: Evaluating Explanatory Power 233 Appendix C: Logistic Regression for More Than Two Classes 235 Problems 239
CHAPTER 11 Neural Nets 242
11.1 Introduction 242
11.2 Concept and Structure of a Neural Network 243
11.3 Fitting a Network to Data 243 Example
1: Tiny Dataset 244 Computing Output of Nodes 245 Preprocessing the Data 248 Training the Model 248 Example
2: Classifying Accident Severity 253 Avoiding overfitting 254 Using the Output for Prediction and Classification 258
11.4 Required User Input 258
11.5 Exploring the Relationship Between Predictors and Response 259
11.6 Advantages and Weaknesses of Neural Networks 261 Problems 262
CHAPTER 12 Discriminant Analysis 264
12.1 Introduction 264 Example
1: Riding Mowers 265 Example
2: Personal Loan Acceptance 265
12.2 Distance of an Observation from a Class 267
12.3 Fisher''s Linear Classification Functions 268
12.4 Classification Performance of Discriminant Analysis 272
12.5 Prior Probabilities 273
12.6 Unequal Misclassification Costs 274
12.7 Classifying More Than Two Classes 274 Example
3: Medical Dispatch to Accident Scenes 274
12.8 Advantages and Weaknesses 277 Problems 279
CHAPTER 13 Combining Methods: Ensembles and Uplift Modeling 282
13.1 Ensembles 282 Why Ensembles Can Improve Predictive Power 283 Simple Averaging 284 Bagging 286 Boosting 286 Advantages and Weaknesses of Ensembles 286
13.2 Uplift (Persuasion) Modeling 287 A-B Testing 287 Uplift 288 Gathering the Data 288 A Simple Model 289 Modeling Individual Uplift 290 Using the Results of an Uplift Model 292
13.3 Summary 292 Problems 293
PART V MINING RELATIONSHIPS AMONG RECORDS
CHAPTER 14 Association Rules and Collaborative Filtering 297
14.1 Association Rules 297 Discovering Association Rules in Transaction Databases 298 Example
1: Purchases of Phone Faceplates 298 Generating Candidate Rules 298 The Apriori Algorithm 301 Selecting Strong Rules 301 Data Format 303 The Process of Rule Selection 304 Interpreting the Results 306 Rules and Chance 306 Example
2: Rules for Similar Book Purchases 308
14.2 Collaborative Filtering1 310 Data Type and Format 311 Example
3: Netflix Prize Contest 311 User-Based Collaborative Filtering: "People Like You" 312 Item-Based Collaborative Filtering 315 Advantages and Weaknesses of Collaborative Filtering 316 Collaborative Filtering vs. Association Rules 316
14.3 Summary 318 Problems 320
CHAPTER 15 Cluster Analysis 324
15.1 Introduction 324 Example: Public Utilities 326
15.2 Measuring Distance Between Two Observations 328 Euclidean Distance 328 Normalizing Numerical Measurements 328 Other Distance Measures for Numerical Data 329 Distance Measures for Categorical Data 331 Distance Measures for Mixed Data 331
15.3 Measuring Distance Between Two Clusters 332
15.4 Hierarchical (Agglomerative) Clustering 334 Single Linkage 335 Complete Linkage 335 Average Linkage 336 Centroid Linkage 336 Dendrograms: Displaying Clustering Process and Results 337 Validating Clusters 339 Limitations of Hierarchical Clustering 34
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