Data mining for business intelligence concepts, techniques, and applications in Microsoft Office Excel with XLMiner
Praise for the First 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 a...
| Otros Autores: | , , |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Hoboken, New Jersey :
Wiley
2010.
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| Edición: | 2nd ed |
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009627766306719 |
Tabla de Contenidos:
- Cover; Title Page; Dedication; Copyright Page; Foreword; Preface to the second edition; Preface to the first edition; Acknowledgments; Part One: Preliminaries; Chapter 1: Introduction; 1.1 What Is Data Mining?; 1.2 Where Is Data Mining Used?; 1.3 Origins of Data Mining; 1.4 Rapid Growth of Data Mining; 1.5 Why Are There So Many Different Methods?; 1.6 Terminology and Notation; 1.7 Road Maps to This Book; Chapter 2: Overview of the Data Mining Process; 2.1 Introduction; 2.2 Core Ideas in Data Mining; 2.3 Supervised and Unsupervised Learning; 2.4 Steps in Data Mining; 2.5 Preliminary Steps
- 2.6 Building a Model: Example with Linear Regression2.7 Using Excel for Data Mining; PROBLEMS; Part Two: Data Exploration and Dimension Reduction; Chapter 3: Data Visualization; 3.1 Uses of Data Visualization; 3.2 Data Examples; 3.3 Basic Charts: bar charts, line graphs, and scatterplots; 3.4 Multidimensional Visualization; 3.5 Specialized Visualizations; 3.6 Summary of major visualizations and operations, according to data mining goal; PROBLEMS; Chapter 4: Dimension Reduction; 4.1 Introduction; 4.2 Practical Considerations; 4.3 Data Summaries; 4.4 Correlation Analysis
- 4.5 Reducing the Number of Categories in Categorical Variables4.6 Converting A Categorical Variable to A Numerical Variable; 4.7 Principal Components Analysis; 4.8 Dimension Reduction Using Regression Models; 4.9 Dimension Reduction Using Classification and Regression Trees; PROBLEMS; Part Three: Performance Evaluation; Chapter 5: Evaluating Classification and Predictive Performance; 5.1 Introduction; 5.2 Judging Classification Performance; 5.3 Evaluating Predictive Performance; PROBLEMS; Part Four: Prediction and Classification Methods; Chapter 6: Multiple Linear Regression; 6.1 Introduction
- 6.2 Explanatory versus Predictive modeling6.3 Estimating the Regression Equation and Prediction; 6.4 Variable Selection in Linear Regression; PROBLEMS; Chapter 7: k-Nearest Neighbors (k-NN); 7.1 k-NN Classifier (categorical outcome); 7.2 k-NN for a Numerical Response; 7.3 Advantages and Shortcomings of k-NN Algorithms; PROBLEMS; Chapter 8: Naive Bayes; 8.1 Introduction; 8.2 Applying the Full (Exact) Bayesian Classifier; 8.3 Advantages and Shortcomings of the Naive Bayes Classifier; PROBLEMS; Chapter 9: Classification and Regression Trees; 9.1 Introduction; 9.2 Classification Trees
- 11.2 Concept And Structure Of A Neural Network
