MARC details
| 000 -LEADER |
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08064cam a22004094i 4500 |
| 001 - CONTROL NUMBER |
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70673309 |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20210316114131.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
060703s2018 ii a b 001 0 eng |
| 010 ## - LIBRARY OF CONGRESS CONTROL NUMBER |
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| LC control number |
2006049016 |
| 015 ## - NATIONAL BIBLIOGRAPHY NUMBER |
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| National bibliography number |
GBA678462 |
| Source |
bnb |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| Cancelled/invalid ISBN |
0470084855 (cloth) |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| Cancelled/invalid ISBN |
9780470084854 (cloth) |
| 035 ## - SYSTEM CONTROL NUMBER |
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| System control number |
(OCoLC)70673309 |
| 040 ## - CATALOGING SOURCE |
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| Original cataloging agency |
DLC |
| Transcribing agency |
DLC |
| Modifying agency |
BAKER |
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UKM |
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C#P |
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YDXCP |
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IXA |
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BTCTA |
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NLGGC |
| Description conventions |
rda |
| 050 00 - LIBRARY OF CONGRESS CALL NUMBER |
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| Classification number |
HF5548.2 |
| Item number |
.S44843 2007 |
| 082 00 - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
005.54 |
| Edition number |
22 |
| Item number |
S.G.D |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Shmueli, Galit, |
| Dates associated with a name |
1971-, |
| Relator term |
author. |
| 245 10 - TITLE STATEMENT |
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| Title |
Data mining for business intelligence : |
| Remainder of title |
concepts, techniques, and applications in Microsoft Office Excel with XLMiner / |
| Statement of responsibility, etc |
Galit Shmueli, Nitin R. Patel, Peter C. Bruce |
| 264 #1 - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
|---|
| Place of publication, distribution, etc |
Hoboken, N.J. : |
| Name of publisher, distributor, etc |
Wiley-Interscience, |
| Date of publication, distribution, etc |
[2007] |
| 264 #3 - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
|---|
| Place of publication, distribution, etc |
Hoboken, N.J. : |
| Name of publisher, distributor, etc |
Wiley-Interscience, |
| Date of publication, distribution, etc |
2018 |
| 300 ## - PHYSICAL DESCRIPTION |
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| Extent |
xviii, 279 pages : |
| Other physical details |
illustrations ; |
| Dimensions |
26 cm |
| 336 ## - CONTENT TYPE |
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rdacontent |
| Content type term |
text |
| 337 ## - MEDIA TYPE |
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| Source |
rdamedia |
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unmediated |
| 338 ## - CARRIER TYPE |
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| Source |
rdacarrier |
| Carrier type term |
volume |
| 500 ## - GENERAL NOTE |
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| General note |
Title page verso: First Printing 2007 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
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| Bibliography, etc |
Includes bibliographical references (pages 271-272) and index |
| 505 0# - FORMATTED CONTENTS NOTE |
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| Formatted contents note |
PART I PRELIMINARIES<br/><br/>CHAPTER 1 Introduction 3<br/><br/>1.1 What Is Business Analytics? 3<br/><br/>1.2 What Is Data Mining? 5<br/><br/>1.3 Data Mining and Related Terms 5<br/><br/>1.4 Big Data 6<br/><br/>1.5 Data Science 7<br/><br/>1.6 Why Are There So Many Different Methods? 8<br/><br/>1.7 Terminology and Notation 9<br/><br/>1.8 Road Maps to This Book 11<br/><br/>Order of Topics 11<br/>CHAPTER 2 Overview of the Data Mining Process 15<br/><br/>2.1 Introduction 15<br/><br/>2.2 Core Ideas in Data Mining 16<br/><br/>2.3 The Steps in Data Mining 19<br/><br/>2.4 Preliminary Steps 21<br/><br/>2.5 Predictive Power and Overfitting 33<br/><br/>2.6 Building a Predictive Model 38<br/><br/>2.7 Using R for Data Mining on a Local Machine 43<br/><br/>2.8 Automating Data Mining Solutions 43<br/><br/>PART II DATA EXPLORATION AND DIMENSION REDUCTION<br/><br/>CHAPTER 3 Data Visualization 55<br/><br/>3.1 Uses of Data Visualization 55<br/><br/>3.2 Data Examples 57<br/><br/>3.3 Basic Charts: Bar Charts, Line Graphs, and Scatter Plots 59<br/><br/>3.4 Multidimensional Visualization 67<br/><br/>3.5 Specialized Visualizations 80<br/><br/>3.6 Summary: Major Visualizations and Operations, by Data Mining Goal 86<br/><br/>CHAPTER 4 Dimension Reduction 91<br/><br/>4.1 Introduction 91<br/><br/>4.2 Curse of Dimensionality 92<br/><br/>4.3 Practical Considerations 92<br/><br/>4.4 Data Summaries 94<br/><br/>4.5 Correlation Analysis 97<br/><br/>4.6 Reducing the Number of Categories in Categorical Variables 99<br/><br/>4.7 Converting a Categorical Variable to a Numerical Variable 99<br/><br/>4.8 Principal Components Analysis 101<br/><br/>4.9 Dimension Reduction Using Regression Models 111<br/><br/>4.10 Dimension Reduction Using Classification and Regression Trees 111<br/><br/>PART III PERFORMANCE EVALUATION<br/><br/>CHAPTER 5 Evaluating Predictive Performance 117<br/><br/>5.1 Introduction 117<br/><br/>5.2 Evaluating Predictive Performance 118<br/><br/>5.3 Judging Classifier Performance 122<br/><br/>5.4 Judging Ranking Performance 136<br/><br/>5.5 Oversampling 140<br/><br/>PART IV PREDICTION AND CLASSIFICATION METHODS<br/><br/>CHAPTER 6 Multiple Linear Regression 153<br/><br/>6.1 Introduction 153<br/><br/>6.2 Explanatory vs. Predictive Modeling 154<br/><br/>6.3 Estimating the Regression Equation and Prediction 156<br/><br/>6.4 Variable Selection in Linear Regression 161 |
| 505 0# - FORMATTED CONTENTS NOTE |
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| Formatted contents note |
CHAPTER 7 k-Nearest Neighbors (kNN) 173<br/><br/>7.1 The k-NN Classifier (Categorical Outcome) 173<br/><br/>7.2 k-NN for a Numerical Outcome 180<br/><br/>7.3 Advantages and Shortcomings of k-NN Algorithms 182<br/><br/>CHAPTER 8 The Naive Bayes Classifier 187<br/><br/>8.1 Introduction 187<br/><br/>8.2 Applying the Full (Exact) Bayesian Classifier 189<br/><br/>8.3 Advantages and Shortcomings of the Naive Bayes Classifier 199<br/><br/>CHAPTER 9 Classification and Regression Trees 205<br/><br/>9.1 Introduction 205<br/><br/>9.2 Classification Trees 207<br/><br/>9.3 Evaluating the Performance of a Classification Tree 215<br/><br/>9.4 Avoiding Overfitting 216<br/><br/>9.5 Classification Rules from Trees 226<br/><br/>9.6 Classification Trees for More Than Two Classes 227<br/><br/>9.7 Regression Trees 227<br/><br/>9.8 Improving Prediction: Random Forests and Boosted Trees 229<br/><br/>9.9 Advantages and Weaknesses of a Tree 232<br/><br/>CHAPTER 10 Logistic Regression 237<br/><br/>10.1 Introduction 237<br/><br/>10.2 The Logistic Regression Model 239<br/><br/>10.3 Example: Acceptance of Personal Loan 240<br/><br/>10.4 Evaluating Classification Performance 247<br/><br/>10.5 Example of Complete Analysis: Predicting Delayed Flights 250<br/><br/>10.6 Appendix: Logistic Regression for Profiling 259<br/><br/>Appendix A: Why Linear Regression Is Problematic for a Categorical Outcome 259<br/><br/>Appendix B: Evaluating Explanatory Power 261<br/><br/>Appendix C: Logistic Regression for More Than Two Classes 264<br/><br/>CHAPTER 11 Neural Nets 271<br/><br/>11.1 Introduction 271<br/><br/>11.2 Concept and Structure of a Neural Network 272<br/><br/>11.3 Fitting a Network to Data 273<br/><br/>11.4 Required User Input 285<br/><br/>11.5 Exploring the Relationship between Predictors and Outcome 287<br/><br/>11.6 Advantages and Weaknesses of Neural Networks 288<br/><br/>CHAPTER 12 Discriminant Analysis 293<br/><br/>12.1 Introduction 293<br/><br/>12.2 Distance of a Record from a Class 296<br/><br/>12.3 Fisher’s Linear Classification Functions 297<br/><br/>12.4 Classification Performance of Discriminant Analysis 300<br/><br/>12.5 Prior Probabilities 302<br/><br/>12.6 Unequal Misclassification Costs 302<br/><br/>12.7 Classifying More Than Two Classes 303<br/><br/>12.8 Advantages and Weaknesses 306<br/><br/>CHAPTER 13 Combining Methods: Ensembles and Uplift Modeling 311<br/><br/>13.1 Ensembles 311<br/><br/>13.2 Uplift (Persuasion) Modeling 317<br/><br/>13.3 Summary 324<br/><br/>PART V MINING RELATIONSHIPS AMONG RECORDS<br/><br/>CHAPTER 14 Association Rules and Collaborative Filtering 329<br/><br/>14.1 Association Rules 329<br/><br/>14.2 Collaborative Filtering 342<br/><br/>14.3 Summary 351 |
| 505 0# - FORMATTED CONTENTS NOTE |
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| Formatted contents note |
CHAPTER 15 Cluster Analysis 357<br/><br/>15.1 Introduction 357<br/><br/>15.2 Measuring Distance between Two Records 361<br/><br/>15.3 Measuring Distance between Two Clusters 366<br/><br/>15.4 Hierarchical (Agglomerative) Clustering 368<br/><br/>15.5 Non-Hierarchical Clustering: The k-Means Algorithm 376<br/><br/>PART VI FORECASTING TIME SERIES<br/><br/>CHAPTER 16 Handling Time Series 387<br/><br/>16.1 Introduction 387<br/><br/>16.2 Descriptive vs. Predictive Modeling 389<br/><br/>16.3 Popular Forecasting Methods in Business 389<br/><br/>16.4 Time Series Components 390<br/><br/>16.5 Data-Partitioning and Performance Evaluation 395<br/><br/>CHAPTER 17 Regression-Based Forecasting 401<br/><br/>17.1 A Model with Trend 401<br/><br/>17.2 A Model with Seasonality 407<br/><br/>17.3 A Model with Trend and Seasonality 411<br/><br/>17.4 Autocorrelation and ARIMA Models 412<br/><br/>CHAPTER 18 Smoothing Methods 433<br/><br/>18.1 Introduction 433<br/><br/>18.2 Moving Average 434<br/><br/>18.3 Simple Exponential Smoothing 439<br/><br/>18.4 Advanced Exponential Smoothing 442<br/><br/>PART VII DATA ANALYTICS<br/><br/>CHAPTER 19 Social Network Analytics 455<br/><br/>19.1 Introduction 455<br/><br/>19.2 Directed vs. Undirected Networks 457<br/><br/>19.3 Visualizing and Analyzing Networks 458<br/><br/>19.4 Social Data Metrics and Taxonomy 462<br/><br/>19.5 Using Network Metrics in Prediction and Classification 467<br/><br/>19.6 Collecting Social Network Data with R 471<br/><br/>19.7 Advantages and Disadvantages 474<br/><br/>CHAPTER 20 Text Mining 479<br/><br/>20.1 Introduction 479<br/><br/>20.2 The Tabular Representation of Text: Term-Document Matrix and “Bag-of-Words” 480<br/><br/>20.3 Bag-of-Words vs. Meaning Extraction at Document Level 481<br/><br/>20.4 Preprocessing the Text 482<br/><br/>20.5 Implementing Data Mining Methods 489<br/><br/>20.6 Example: Online Discussions on Autos and Electronics 490<br/><br/>20.7 Summary 494<br/><br/>PART VIII CASES<br/><br/>CHAPTER 21 Cases 499<br/><br/>21.1 Charles Book Club 499<br/><br/>21.2 German Credit 505<br/><br/>21.3 Tayko Software Cataloger 510<br/><br/>21.4 Political Persuasion 513<br/><br/>21.5 Taxi Cancellations 517<br/><br/>21.6 Segmenting Consumers of Bath Soap 518<br/><br/>21.7 Direct-Mail Fundraising 521<br/><br/>21.8 Catalog Cross-Selling 524<br/><br/>21.9 Predicting Bankruptcy 525<br/><br/>21.10 Time Series Case: Forecasting Public Transportation Demand 528<br/><br/>Index 535 |
| 630 00 - SUBJECT ADDED ENTRY--UNIFORM TITLE |
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| Uniform title |
Microsoft Excel (Computer file) |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Business |
| General subdivision |
Data processing |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Data mining |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Patel, Nitin R. |
| Fuller form of name |
(Nitin Ratilal), |
| Relator term |
author. |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Bruce, Peter C., |
| Dates associated with a name |
1953-, |
| Relator term |
author. |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Source of classification or shelving scheme |
Dewey Decimal Classification |
| Koha item type |
Books |