Data mining for business analytics: concepts, techniques and applications in R (Record no. 316)
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| 000 -LEADER | |
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| fixed length control field | 07458nam a22002417a 4500 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20190831113535.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 190831b ||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9788126577392 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 005.54 |
| Item number | SHM |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Shmueli, Galit |
| 245 ## - TITLE STATEMENT | |
| Title | Data mining for business analytics: concepts, techniques and applications in R |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Name of publisher, distributor, etc. | Wiley India Pvt. Ltd. |
| Place of publication, distribution, etc. | New Delhi |
| Date of publication, distribution, etc. | 2019 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | xxix, 544 p. |
| 365 ## - TRADE PRICE | |
| Price type code | INR |
| Price amount | 899.00 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE | |
| Bibliography, etc. note | Contents<br/><br/>Foreword by Gareth James<br/><br/>Foreword by Ravi Bapna<br/><br/>Preface to the R Edition<br/><br/>Acknowledgments<br/><br/> <br/><br/>Part I Preliminaries<br/><br/>Chapter 1 Introduction<br/><br/>1.1 What Is Business Analytics? <br/><br/>1.2 What Is Data Mining? <br/><br/>1.3 Data Mining and Related Terms <br/><br/>1.4 Big Data <br/><br/>1.5 Data Science <br/><br/>1.6 Why Are There So Many Different Methods? <br/><br/>1.7 Terminology and Notation <br/><br/>1.8 Road Maps to This Book<br/><br/> <br/><br/>Chapter 2 Overview of the Data Mining Process<br/><br/>2.1 Introduction<br/><br/>2.2 Core Ideas in Data Mining<br/><br/>2.3 The Steps in Data Mining<br/><br/>2.4 Preliminary Steps<br/><br/>2.5 Predictive Power and Overfitting<br/><br/>2.6 Building a Predictive Model<br/><br/>2.7 Using R for Data Mining on a Local Machine<br/><br/>2.8 Automating Data Mining Solutions<br/><br/> <br/><br/>Part II Data Exploration and Dimension Reduction<br/><br/>Chapter 3 Data Visualization<br/><br/>3.1 Uses of Data Visualization<br/><br/>3.2 Data Examples<br/><br/>3.3 Basic Charts: Bar Charts, Line Graphs, and Scatter Plots<br/><br/>3.4 Multidimensional Visualization<br/><br/>3.5 Specialized Visualizations<br/><br/>3.6 Summary: Major Visualizations and Operations, by Data Mining Goal<br/><br/> <br/><br/>Chapter 4 Dimension Reduction<br/><br/>4.1 Introduction<br/><br/>4.2 Curse of Dimensionality<br/><br/>4.3 Practical Considerations<br/><br/>4.4 Data Summaries<br/><br/>4.5 Correlation Analysis<br/><br/>4.6 Reducing the Number of Categories in Categorical Variables<br/><br/>4.7 Converting a Categorical Variable to a Numerical Variable<br/><br/>4.8 Principal Components Analysis<br/><br/>4.9 Dimension Reduction Using Regression Models<br/><br/>4.10 Dimension Reduction Using Classification and Regression Trees<br/><br/> <br/><br/>Part III Performance Evaluation<br/><br/>Chapter 5 Evaluating Predictive Performance<br/><br/>5.1 Introduction<br/><br/>5.2 Evaluating Predictive Performance<br/><br/>5.3 Judging Classifier Performance<br/><br/>5.4 Judging Ranking Performance<br/><br/>5.5 Oversampling<br/><br/> <br/><br/>Part IV Prediction and Classification Methods<br/><br/>Chapter 6 Multiple Linear Regression<br/><br/>6.1 Introduction<br/><br/>6.2 Explanatory vs. Predictive Modeling<br/><br/>6.3 Estimating the Regression Equation and Prediction<br/><br/>6.4 Variable Selection in Linear Regression<br/><br/> <br/><br/>Chapter 7 k-Nearest Neighbors (kNN)<br/><br/>7.1 The k-NN Classifier (Categorical Outcome)<br/><br/>7.2 k-NN for a Numerical Outcome<br/><br/>7.3 Advantages and Shortcomings of k-NN Algorithms<br/><br/> <br/><br/>Chapter 8 The Naive Bayes Classifier<br/><br/>8.1 Introduction<br/><br/>8.2 Applying the Full (Exact) Bayesian Classifier<br/><br/>8.3 Advantages and Shortcomings of the Naive Bayes Classifier<br/><br/> <br/><br/>Chapter 9 Classification and Regression Trees<br/><br/>9.1 Introduction<br/><br/>9.2 Classification Trees<br/><br/>9.3 Evaluating the Performance of a Classification Tree<br/><br/>9.4 Avoiding Overfitting<br/><br/>9.5 Classification Rules from Trees<br/><br/>9.6 Classification Trees for More Than Two Classes<br/><br/>9.7 Regression Trees<br/><br/>9.8 Improving Prediction: Random Forests and Boosted Trees<br/><br/>9.9 Advantages and Weaknesses of a Tree<br/><br/> <br/><br/>Chapter 10 Logistic Regression<br/><br/>10.1 Introduction<br/><br/>10.2 The Logistic Regression Model<br/><br/>10.3 Example: Acceptance of Personal Loan<br/><br/>10.4 Evaluating Classification Performance<br/><br/>10.5 Example of Complete Analysis: Predicting Delayed Flights<br/><br/>10.6 Appendix: Logistic Regression for Profiling<br/><br/> <br/><br/>Chapter 11 Neural Nets<br/><br/>11.1 Introduction<br/><br/>11.2 Concept and Structure of a Neural Network<br/><br/>11.3 Fitting a Network to Data<br/><br/>11.4 Required User Input<br/><br/>11.5 Exploring the Relationship Between Predictors and Outcome<br/><br/>11.6 Advantages and Weaknesses of Neural Networks<br/><br/> <br/><br/>Chapter 12 Discriminant Analysis<br/><br/>12.1 Introduction<br/><br/>12.2 Distance of a Record from a Class<br/><br/>12.3 Fisher's Linear Classification Functions<br/><br/>12.4 Classification Performance of Discriminant Analysis<br/><br/>12.5 Prior Probabilities<br/><br/>12.6 Unequal Misclassification Costs<br/><br/>12.7 Classifying More Than Two Classes<br/><br/>12.8 Advantages and Weaknesses<br/><br/> <br/><br/>Chapter 13 Combining Methods: Ensembles and Uplift Modeling<br/><br/>13.1 Ensembles<br/><br/>13.2 Uplift (Persuasion) Modeling<br/><br/>13.3 Summary<br/><br/> <br/><br/>Part V Mining Relationships Among Records<br/><br/>Chapter 14 Association Rules and Collaborative Filtering<br/><br/>14.1 Association Rules<br/><br/>14.2 Collaborative Filtering<br/><br/>14.3 Summary<br/><br/> <br/><br/>Chapter 15 Cluster Analysis<br/><br/>15.1 Introduction<br/><br/>15.2 Measuring Distance Between Two Records<br/><br/>15.3 Measuring Distance Between Two Clusters<br/><br/>15.4 Hierarchical (Agglomerative) Clustering<br/><br/>15.5 Non-Hierarchical Clustering: The k-Means Algorithm<br/><br/> <br/><br/>Part VI Forecasting Time Series<br/><br/>Chapter 16 Handling Time Series<br/><br/>16.1 Introduction<br/><br/>16.2 Descriptive vs. Predictive Modeling<br/><br/>16.3 Popular Forecasting Methods in Business<br/><br/>16.4 Time Series Components<br/><br/>16.5 Data-Partitioning and Performance Evaluation<br/><br/> <br/><br/>Chapter 17 Regression-Based Forecasting<br/><br/>17.1 A Model with Trend<br/><br/>17.2 A Model with Seasonality<br/><br/>17.3 A Model with Trend and Seasonality<br/><br/>17.4 Autocorrelation and ARIMA Models<br/><br/> <br/><br/>Chapter 18 Smoothing Methods<br/><br/>18.1 Introduction<br/><br/>18.2 Moving Average<br/><br/>18.3 Simple Exponential Smoothing<br/><br/>18.4 Advanced Exponential Smoothing<br/><br/> <br/><br/>Part VII Data Analytics<br/><br/>Chapter 19 Social Network Analytics<br/><br/>19.1 Introduction<br/><br/>19.2 Directed vs. Undirected Networks<br/><br/>19.3 Visualizing and Analyzing Networks<br/><br/>19.4 Social Data Metrics and Taxonomy<br/><br/>19.5 Using Network Metrics in Prediction and Classification<br/><br/>19.6 Collecting Social Network Data with R<br/><br/>19.7 Advantages and Disadvantages<br/><br/> <br/><br/>Chapter 20 Text Mining<br/><br/>20.1 Introduction<br/><br/>20.2 The Tabular Representation of Text: Term-Document Matrix and "Bag-of-Words"<br/><br/>20.3 Bag-of-Words vs. Meaning Extraction at Document Level<br/><br/>20.4 Preprocessing the Text<br/><br/>20.5 Implementing Data Mining Methods<br/><br/>20.6 Example: Online Discussions on Autos and Electronics<br/><br/>20.7 Summary<br/><br/> <br/><br/>Part VIII Cases<br/><br/>Chapter 21 Cases<br/><br/>21.1 Charles Book Club<br/><br/>21.2 German Credit<br/><br/>21.3 Tayko Software Cataloger<br/><br/>21.4 Political Persuasion<br/><br/>21.5 Taxi Cancellations<br/><br/>21.6 Segmenting Consumers of Bath Soap<br/><br/>21.7 Direct-Mail Fundraising<br/><br/>21.8 Catalog Cross-Selling<br/><br/>21.9 Predicting Bankruptcy<br/><br/>21.10 Time Series Case: Forecasting Public Transportation Demand<br/><br/> <br/><br/>References<br/><br/>Data Files Used in the Book<br/><br/>Index |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | This book has by far the most comprehensive review of business analytics methods that I have ever seen, covering everything from classical approaches such as linear and logistic regression, through to modern methods like neural networks, bagging and boosting, and even much more business specific procedures such as social network analysis and text mining. If not the bible, it is at the least a definitive manual on the subject.Incorporating an innovative focus on data visualization and time series forecasting, Data Mining for Business Analytics supplies insightful, detailed guidance on fundamental data mining techniques. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Business -- Data processing |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Data mining |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Bruce, Peter C. |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Yahav, Inbal |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Patel, Nitin R. |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Dewey Decimal Classification |
| Koha item type | Book |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Collection code | Home library | Current library | Shelving location | Date acquired | Source of acquisition | Cost, normal purchase price | Total Checkouts | Total Renewals | Full call number | Accession Number | Checked out | Date last seen | Date checked out | Copy number | Cost, replacement price | Price effective from | Koha item type | Bill No | Bill Date |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dewey Decimal Classification | IT & Decisions Sciences | Indian Institute of Management LRC | Indian Institute of Management LRC | General Stacks | 06/17/2019 | Overseas Press India Private | 673.35 | 4 | 3 | 005.54 SHM | 000595 | 10/24/2021 | 07/26/2021 | 07/26/2021 | 1 | 899.00 | 08/31/2019 | Book | IN28349 | 22-05-2019 |