Data mining for business analytics: concepts, techniques, and applications in R
Gespeichert in:
| Beteiligte Personen: | , , , , |
|---|---|
| Format: | Buch |
| Sprache: | Englisch |
| Veröffentlicht: |
Hoboken, NJ
Wiley
2018
|
| Schlagwörter: | |
| Links: | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029938325&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| Beschreibung: | Enthält Index |
| Umfang: | XXIX, 544 Seiten Illustrationen, Diagramme |
| ISBN: | 9781118879368 |
Internformat
MARC
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| 245 | 1 | 0 | |a Data mining for business analytics |b concepts, techniques, and applications in R |c Galit Shmueli, Peter C. Bruce, Inbal Yahav, Nitin R. Patel, Kenneth C. Lichtendahl, Jr. |
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Datensatz im Suchindex
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| adam_text | ■
Contents___________________________________________________________________________
Foreword by Gareth James x1x
Foreword by Ravi Bapna xxi
Preface to the R Edition xxiii
Acknowledgments xxvii
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...................................................... 11
Chapter 2 Overview of the Data Mining Process is
2.1 Introduction........................................................... 15
2.2 Core Ideas in Data Mining.............................................. 16
Classification......................................................... 16
Prediction............................................................. 16
Association Rules and Recommendation Systems........................... 16
Predictive Analytics................................................. 17
Data Reduction and Dimension Reduction................................. 17
Data Exploration and Visualization..................................... 17
Supervised and Unsupervised Learning................................... 18
2.3 The Steps in Data Mining............................................. 19
2.4 Preliminary Steps..................................................... 21
Organization of Datasets............................................... 21
Predicting Home Values in the West Roxbury Neighborhood .............. 21
VII
VIII CONTENTS
Loading and Looking at the Data in R........................................... . 22
Sampling from a Database....................................................... 24
Oversampling Rare Events in Classification Tasks............................... 25
Preprocessing and Cleaning the Data ........................................... 26
2.5 Predictive Power and Overfitting................................................. 33
Overfitting .................................................................... 33
Creation and Use of Data Partitions............................................ 35
2.6 Building a Predictive Model...................................................... 38
Modeling Process............................................................... 39
2.7 Using R for Data Mining on a Local Machine...................................... 43
2.8 Automating Data Mining Solutions................................................. 43
Data Mining Software: The State of the Market (by Herb Edelstein).............. 45
Problems............................................................................... 49
Part n data exploration and dimension reduction
chapter 3 Data Visualization 55
3.1 Uses of Data Visualization..................................................... 55
Base R or ggplot?................................................................ 57
3.2 Data Examples.................................................................... 57
Example 1: Boston Housing Data................................................... 57
Example 2: Riders hip on Amtrak Trains......................................... 59
3.3 Basic Charts: Bar Charts, Line Graphs, and Scatter Plots ....................... 59
Distribution Plots: Boxplots and Histograms....................................... 61
Heatmaps: Visualizing Correlations and Missing Values............................. 64
3.4 Multidimensional Visualization................................................... 67
Adding Variables: Color, Size, Shape, Multiple Panels, and Animation........... 67
Manipulations: Rescaling, Aggregation and Hierarchies, Zooming, Filtering .... 70
Reference: Trend Lines and Labels................................................ 74
Scaling up to Large Datasets................................................... 74
Multivariate Plot: Parallel Coordinates Plot. ................................. 75
Interactive Visualization...................................................... 77
3.5 Specialized Visualizations....................................................... 80
Visualizing Networked Data..................................................... 80
Visualizing Hierarchical Data: Treemaps........................................ 82
Visualizing Geographical Data: Map Charts........................................ 83
3.6 Summary: Major Visualizations and Operations, by Data Mining Goal................ 86
Prediction...................................................................... . 86
Classification .................................................................. 86
Time Series Forecasting............................................................ 86
Unsupervised Learning.............................................................. 87
Problems................................................................................ g$
chapter 4 Dimension Reduction 91
4.1 Introduction.................................................................... 91
4.2 Curse of Dimensionality....................................................... 92
CONTENTS IX
4.3 Practical Considerations....................................................... 92
Example 1: House Prices in Boston ............................................. 93
4.4 Data Summaries................................................................. 94
Summary Statistics........................................................... 94
Aggregation and Pivot Tables................................................. 96
4.5 Correlation Analysis......................................................... 97
4.6 Reducing the Number of Categories in Categorical Variables..................... 99
4.7 Converting a Categorical Variable to a Numerical Variable ................. 99
4.8 Principal Components Analysis................................................ 101
Example 2: Breakfast Cereals ..................................................101
Principal Components...........................................................106
Normalizing the Data ..........................................................107
Using Principal Components for Classification and Prediction...................109
4.9 Dimension Reduction Using Regression Models....................................Ill
4.10 Dimension Reduction Using Classification and Regression Trees..................Ill
Problems..............................................................................112
Part m performance evaluation________________________________________________________________
Chapter 5 Evaluating Predictive Performance 117
5.1 Introduction...................................................................117
5.2 Evaluating Predictive Performance..............................................118
Naive Benchmark: The Average ................................................ 118
Prediction Accuracy Measures...................................................119
Comparing Training and Validation Performance..................................121
Lift Chart.....................................................................121
5.3 Judging Classifier Performance.................................................122
Benchmark: The Naive Rule.................................................... 124
Class Separation............................................................. 124
The Confusion (Classification) Matrix..........................................124
Using the Validation Data.................................................... 126
Accuracy Measures..............................................................126
Propensities and Cutoff for Classification ............................. 127
Performance in Case of Unequal Importance of Classes...........................131
Asymmetric Misclassification Costs........................................... 133
Generalization to More Than Two Classes........................................135
5.4 Judging Ranking Performance....................................................136
Lift Charts for Binary Data....................................................136
Decile Lift Charts.............................................................138
Beyond Two Classes.............................................................139
Lift Charts Incorporating Costs and Benefits...................................139
Lift as a Function of Cutoff...................................................140
5.5 Oversampling................................................................. 140
Oversampling the Training Set..................................................144
X CONTENTS
Evaluating Model Performance Using a Non-oversampled Validation Set..........144
Evaluating Model Performance if Only Oversampled Validation Set Exists ..... 144
Problems...........................................................................147
Part iv prediction and classification methods________________________________________________
Chapter 6 Multiple Linear Regression 153
6.1 Introduction.................................................................153
6.2 Explanatory vs. Predictive Modeling .........................................154
6.3 Estimating the Regression Equation and Prediction..............................156
Example: Predicting the Price of Used Toyota Corolla Cars....................156
6.4 Variable Selection in Linear Regression .......................................161
Reducing the Number of Predictors ...........................................161
How to Reduce the Number of Predictors.......................................162
Problems........................................................................... 169
Chapter 7 fc-Nearest Neighbors (feNN) 173
7.1 The A NN Classifier (Categorical Outcome)..................................... 173
Determining Neighbors..........................................................173
Classification Rule........................................................... 174
Example: Riding Mowers.........................................................175
Choosing k.....................................................................176
Setting the Cutoff Value.......................................................179
fc-NN with More Than Two Classes...............................................180
Converting Categorical Variables to Binary Dummies ............................180
7.2 fc-NN for a Numerical Outcome.......................................... 180
7.3 Advantages and Shortcomings of fc-NN Algorithms................................182
Problems...........................................................................184
chapter 8 The Naive Bayes Classifier 187
8.1 Introduction...................................................................187
Cutoff Probability Method..................................................... 188
Conditional Probability....................................................... 188
Example 1: Predicting Fraudulent Financial Reporting...........................188
8.2 Applying the Full (Exact) Bayesian Classifier..................................189
Using the Assign to the Most Probable Class Method..........................190
Using the Cutoff Probability Method...........................................190
Practical Difficulty with the Complete (Exact) Bayes Procedure................190
Solution: Naive Bayes...........................................................291
The Naive Bayes Assumption of Conditional Independence........................192
Using the Cutoff Probability Method.............................................192
Example 2: Predicting Fraudulent Financial Reports, Two Predictors ...........193
Example 3: Predicting Delayed Flights.......................................... 194
8.3 Advantages and Shortcomings of the Naive Bayes Classifier ...................199
Problems............................................................................202
CONTENTS XI
chapter 9 Classification and Regression Trees 205
9.1 Introduction..................................................................... 205
9.2 Classification Trees ..............................................................207
Recursive Partitioning............................................................207
Example 1: Riding Mowers..........................................................207
Measures of Impurity..............................................................210
Tree Structure....................................................................214
Classifying a New Record ....................................................... 214
9.3 Evaluating the Performance of a Classification Tree................................215
Example 2: Acceptance of Personal Loan.......................................... 215
9.4 Avoiding Overfitting............................................................. 216
Stopping Tree Growth: Conditional Inference Trees.................................221
Pruning the Tree..................................................................222
Cross-Validation..................................................................222
Best-Pruned Tree .................................................................224
9.5 Classification Rules from Trees.................................................. 226
9.6 Classification Trees for More Than Two Classes................................... 227
9.7 Regression Trees...................................................................227
Prediction........................................................................228
Measuring Impurity ...............................................................228
Evaluating Performance ...........................................................229
9.8 Improving Prediction: Random Forests and Boosted Trees ............................229
Random Forests.................................................................. 229
Boosted Trees................................................................... 231
9.9 Advantages and Weaknesses of a Tree................................................232
Problems.................................................................................234
Chapter lO Logistic Regression 237
10.1 Introduction.......................................................................237
10.2 The Logistic Regression Model.................................................... 239
10.3 Example: Acceptance of Personal Loan............................................. 240
Model with a Single Predictor.....................................................241
Estimating the Logistic Model from Data: Computing Parameter Estimates .... 243
Interpreting Results in Terms of Odds (for a Profiling Goal)......................244
10.4 Evaluating Classification Performance..............................................247
Variable Selection.............................................................. 248
10.5 Example of Complete Analysis: Predicting Delayed Flights...........................250
Data Preprocessing.............................................................. 251
Model-Fitting and Estimation .....................................................254
Model Interpretation ........................................................... 254
Model Performance ................................................................254
Variable Selection................................................................257
10.6 Appendix: Logistic Regression for Profiling........................................259
Appendix A: Why Linear Regression Is Problematic for a Categorical Outcome . . . 259
XII CONTENTS
Appendix B: Evaluating Explanatory Power.......................................261
Appendix C: Logistic Regression for More Than Two Classes.....................264
Problems.............................................................................268
chapter 1 1 Neural Nets 271
11.1 Introduction...................................................................271
11.2 Concept and Structure of a Neural Network......................................272
11.3 Fitting a Network to Data......................................................273
Example 1: Tiny Dataset...................................................... 273
Computing Output of Nodes......................................................274
Preprocessing the Data.........................................................277
Training the Model.............................................................278
Example 2: Classifying Accident Severity ......................................282
Avoiding Overfitting...........................................................283
Using the Output for Prediction and Classification...................... 283
11.4 Required User Input.......................................................... 285
11.5 Exploring the Relationship Between Predictors and Outcome......................287
11.6 Advantages and Weaknesses of Neural Networks...................................288
Problems............................................................................. 290
chapter 12 Discriminant Analysis 293
12.1 Introduction...................................................................293
Example 1: Riding Mowers.......................................................294
Example 2: Personal Loan Acceptance............................................294
12.2 Distance of a Record from a Class............................................ 296
12.3 Fisher s Linear Classification Functions.......................................297
12.4 Classification Performance of Discriminant Analysis ...........................300
12.5 Prior Probabilities.......................................................... 302
12.6 Unequal Misclassification Costs................................................302
12.7 Classifying More Than Two Classes..............................................303
Example 3: Medical Dispatch to Accident Scenes..................................303
12.8 Advantages and Weaknesses......................................................306
Problems........................................................................ 307
chapter 1 3 Combining Methods: Ensembles and Uplift Modeling 311
13.1 Ensembles......................................................................311
Why Ensembles Can Improve Predictive Power......................................312
Simple Averaging.............................................................. 314
Bagging.........................................................................315
Boosting .......................................................................315
Bagging and Boosting in R ......................................................315
Advantages and Weaknesses of Ensembles..........................................315
13.2 Uplift (Persuasion) Modeling...................................................317
A-B Testing .................................................................. 318
CONTENTS XIII
Uplift....................................................................318
Gathering the Data...........................................................319
A Simple Model...............................................................320
Modeling Individual Uplift...................................................321
Computing Uplift with R......................................................322
Using the Results of an Uplift Model......................................322
13.3 Summary...................................................................324
Problems ..........................................................................325
Part v mining relationships among records_________________________________________________
chapter 1 4 Association Rules and Collaborative Filtering 329
14.1 Association Rules............................................................329
Discovering Association Rules in Transaction Databases.......................330
Example 1: Synthetic Data on Purchases of Phone Faceplates ..................330
Generating Candidate Rules...................................................330
The Apriori Algorithm...................................................... 333
Selecting Strong Rules.......................................................333
Data Format..................................................................335
The Process of Rule Selection................................................336
Interpreting the Results.....................................................337
Rules and Chance......................................................... 339
Example 2: Rules for Similar Book Purchases .................................340
14.2 Collaborative Filtering......................................................342
Data Type and Format.........................................................343
Example 3: Netflix Prize Contest.............................................343
User-Based Collaborative Filtering: People Like You ........................344
Item-Based Collaborative Filtering...........................................347
Advantages and Weaknesses of Collaborative Filtering.........................348
Collaborative Filtering vs. Association Rules................................349
14.3 Summary.................................................................... 351
Problems...........................................................................352
chapter 15 Cluster Analysis 357
15.1 Introduction............................................................... 357
Example: Public Utilities....................................................359
15.2 Measuring Distance Between Two Records.......................................361
Euclidean Distance ..........................................................361
Normalizing Numerical Measurements...........................................362
Other Distance Measures for Numerical Data................................. 362
Distance Measures for Categorical Data ......................................365
Distance Measures for Mixed Data.............................................366
15.3 Measuring Distance Between Two Clusters......................................366
Minimum Distance.............................................................366
Maximum Distance.............................................................366
XIV CONTENTS
Average Distance..............................................................367
Centroid Distance............................*..............................367
15.4 Hierarchical (Agglomerative) Clustering..................................... 368
Single Linkage................................................................369
Complete Linkage..............................................................370
Average Linkage.............................................................370
Centroid Linkage............................................................370
Ward s Method...............................................................370
Dendrograms: Displaying Clustering Process and Results........................371
Validating Clusters...........................................................373
Limitations of Hierarchical Clustering .......................................375
15.5 Non-Hierarchical Clustering: The fc-Means Algorithm...........................376
Choosing the Number of Clusters (k)...........................................377
Problems.......................................................................... 382
Part vi forecasting time series____________________________________________________________
chapter 16 Handling Time Series 387
16.1 Introduction................................................................387
16.2 Descriptive vs. Predictive Modeling...........................................389
16.3 Popular Forecasting Methods in Business.......................................389
Combining Methods.............................................................389
16.4 Time Series Components........................................................390
Example: Ridership on Amtrak Trains......................................... 390
16.5 Data-Partitioning and Performance Evaluation..................................395
Benchmark Performance: Naive Forecasts .......................................395
Generating Future Forecasts...................................................396
Problems.......................................................................... 398
Chapter 17 Regression-Based Forecasting 401
17.1 A Model with Trend............................................................401
Linear Trend..................................................................401
Exponential Trend.............................................................405
Polynomial Trend .............................................................407
17.2 A Model with Seasonality......................................................407
17.3 A Model with Trend and Seasonality............................................411
17.4 Autocorrelation and A RIM A Models........................................... 412
Computing Autocorrelation .....................................................413
Improving Forecasts by Integrating Autocorrelation Information.................416
Evaluating Predictability......................................................420
Problems..........................................................................422
CONTENTS XV
chapter 1 8 Smoothing Methods 433
18.1 Introduction.................................................................433
18.2 Moving Average...............................................................434
Centered Moving Average for Visualization....................................434
Trailing Moving Average for Forecasting......................................435
Choosing Window Width (to).................................................439
18.3 Simple Exponential Smoothing.................................................439
Choosing Smoothing Parameter ex..............................................440
Relation Between Moving Average and Simple Exponential Smoothing.............440
18.4 Advanced Exponential Smoothing...............................................442
Series with a Trend..........................................................442
Series with a Trend and Seasonality . .......................................443
Series with Seasonality (No Trend)...........................................443
Problems......................................................................... 446
Part JVii data analytics__________________________________________________________________
chapter 1 9 Social Network Analytics 455
19.1 Introduction ................................................................455
19.2 Directed vs. Undirected Networks.............................................457
19.3 Visualizing and Analyzing Networks...........................................458
Graph Layout............................................................... 458
Edge List....................................................................460
Adjacency Matrix.............................................................461
Using Network Data in Classification and Prediction..........................461
19.4 Social Data Metrics and Taxonomy........................................... 462
Node-Level Centrality Metrics ...............................................463
Egocentric Network......................................................... 463
Network Metrics..............................................................465
19.5 Using Network Metrics in Prediction and Classification.......................467
Link Prediction............................................................ 467
Entity Resolution............................................................467
Collaborative Filtering.................................................... 468
19.6 Collecting Social Network Data with R........................................471
19.7 Advantages and Disadvantages ................................................474
Problems......................................................................... 476
Chapter 20 Text Mining 479
20.1 Introduction.................................................................479
20.2 The Tabular Representation of Text: Term-Document Matrix and Bag-of--Words . 480
20.3 Bag-of-Words vs. Meaning Extraction at Document Level........................481
20.4 Preprocessing the Text.......................................................482
Tokenization.................................................................484
Text Reduction ..............................................................485
XVI CONTENTS
Presence/Absence vs. Frequency................................................487
Term Frequency-Inverse Document Frequency (TF-IDF)............................487
From Terms to Concepts: Latent Semantic Indexing .............................488
Extracting Meaning............................................................489
20.5 Implementing Data Mining Methods..............................................489
20.6 Example: Online Discussions on Autos and Electronics..........................490
Importing and Labeling the Records............................................490
Text Preprocessing in R.......................................................491
Producing a Concept Matrix....................................................491
Fitting a Predictive Model....................................................492
Prediction....................................................................492
20.7 Summary.......................................................................494
Problems...........................................................................495
Part vih cases_____________________________________________________________________________
CHAPTER 21 Cases 499
21.1 Charles Book Club........................................................... 499
The Book Industry.............................................................499
Database Marketing at Charles.................................................500
Data Mining Techniques........................................................502
Assignment....................................................................504
21.2 German Credit............................................................... 505
Background....................................................................505
Data....................................................................... . 506
Assignment....................................................................507
21.3 Tayko Software Cataloger......................................................510
Background....................................................................510
The Mailing Experiment........................................................510
Data..........................................................................510
Assignment....................................................................512
21.4 Political Persuasion..........................................................513
Background....................................................................513
Predictive Analytics Arrives in US Politics...................................513
Political Targeting...........................................................514
Uplift....................................................................... 514
Data........................................................................ 515
Assignment....................................................................516
21.5 Taxi Cancellations.......................................................... 517
Business Situation............................................................517
Assignment.................................................................. 517
21.6 Segmenting Consumers of Bath Soap.............................................518
Business Situation............................................................518
Key Problems..................................................................519
Data..........................................................................519
CONTENTS XVII
Measuring Brand Loyalty.......................................................519
Assignment................................................................. . 521
21.7 Direct-Mail Fundraising.......................................................521
Background....................................................................521
Data...................................................................... 522
Assignment....................................................................523
21.8 Catalog Cross-Selling . ......................................................524
Background.................................................................. 524
Assignment....................................................................524
21.9 Predicting Bankruptcy.........................................................525
Predicting Corporate Bankruptcy...............................................525
Assignment.................................................................. 526
21.10 Time Series Case: Forecasting Public Transportation Demand...................528
Background....................................................................528
Problem Description...........................................................528
Available Data................................................................528
Assignment Goal...............................................................528
Assignment....................................................................529
Tips and Suggested Steps......................................................529
References 531
Data Files Used in the Book 533
Index
535
|
| any_adam_object | 1 |
| author | Shmueli, Galit 1971- Bruce, Peter C. 1953- Yahav, Inbal Patel, Nitin R. Lichtendahl, Kenneth C. 1969- |
| author_GND | (DE-588)137189265 (DE-588)1104275260 (DE-588)1203765983 (DE-588)170703592 (DE-588)1138054585 |
| author_facet | Shmueli, Galit 1971- Bruce, Peter C. 1953- Yahav, Inbal Patel, Nitin R. Lichtendahl, Kenneth C. 1969- |
| author_role | aut aut aut aut aut |
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| callnumber-subject | HF - Commerce |
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| ctrlnum | (OCoLC)1001432994 (DE-599)BVBBV044539208 |
| dewey-full | 006.3/12 |
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| dewey-ones | 006 - Special computer methods |
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| dewey-sort | 16.3 212 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik Wirtschaftswissenschaften |
| format | Book |
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| id | DE-604.BV044539208 |
| illustrated | Illustrated |
| indexdate | 2024-12-20T18:05:32Z |
| institution | BVB |
| isbn | 9781118879368 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-029938325 |
| oclc_num | 1001432994 |
| open_access_boolean | |
| owner | DE-1102 DE-898 DE-BY-UBR DE-M347 DE-384 DE-739 DE-20 DE-1049 DE-29T DE-634 DE-473 DE-BY-UBG DE-188 |
| owner_facet | DE-1102 DE-898 DE-BY-UBR DE-M347 DE-384 DE-739 DE-20 DE-1049 DE-29T DE-634 DE-473 DE-BY-UBG DE-188 |
| physical | XXIX, 544 Seiten Illustrationen, Diagramme |
| publishDate | 2018 |
| publishDateSearch | 2018 |
| publishDateSort | 2018 |
| publisher | Wiley |
| record_format | marc |
| spellingShingle | Shmueli, Galit 1971- Bruce, Peter C. 1953- Yahav, Inbal Patel, Nitin R. Lichtendahl, Kenneth C. 1969- Data mining for business analytics concepts, techniques, and applications in R Datenverarbeitung Wirtschaft Business mathematics Computer programs Business Data processing Data mining Data Mining (DE-588)4428654-5 gnd R Programm (DE-588)4705956-4 gnd Wissensmanagement (DE-588)4561842-2 gnd |
| subject_GND | (DE-588)4428654-5 (DE-588)4705956-4 (DE-588)4561842-2 |
| title | Data mining for business analytics concepts, techniques, and applications in R |
| title_auth | Data mining for business analytics concepts, techniques, and applications in R |
| title_exact_search | Data mining for business analytics concepts, techniques, and applications in R |
| title_full | 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. |
| title_fullStr | 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. |
| title_full_unstemmed | 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. |
| title_short | Data mining for business analytics |
| title_sort | data mining for business analytics concepts techniques and applications in r |
| title_sub | concepts, techniques, and applications in R |
| topic | Datenverarbeitung Wirtschaft Business mathematics Computer programs Business Data processing Data mining Data Mining (DE-588)4428654-5 gnd R Programm (DE-588)4705956-4 gnd Wissensmanagement (DE-588)4561842-2 gnd |
| topic_facet | Datenverarbeitung Wirtschaft Business mathematics Computer programs Business Data processing Data mining Data Mining R Programm Wissensmanagement |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029938325&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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