Data Mining for Business Analytics Concepts, Techniques, and Applications with XLMiner.

Detalles Bibliográficos
Autor principal: Bruce, Peter C.
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Newark : John Wiley & Sons, Incorporated, 2016.
Colección:New York Academy of Sciences Ser.
Temas:
Electronic books.
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Intro
  • Title Page
  • Copyright
  • Dedication
  • Foreword
  • Preface to the Third Edition
  • Preface to the First Edition
  • Acknowledgments
  • Part I: Preliminaries
  • Chapter 1: Introduction
  • 1.1 What is Business Analytics?
  • 1.2 What is Data Mining?
  • 1.3 Data Mining and Related Terms
  • 1.4 Big Data
  • 1.5 Data Science
  • 1.6 Why are There so Many Different Methods?
  • 1.7 Terminology and Notation
  • 1.8 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 The Steps in Data Mining
  • 2.4 Preliminary Steps
  • 2.5 Predictive Power and Overfitting
  • 2.6 Building a Predictive Model with XLMiner
  • 2.7 Using Excel for Data Mining
  • 2.8 Automating Data Mining Solutions
  • Problems
  • Part II: 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 Scatter Plots
  • 3.4 Multidimensional Visualization
  • 3.5 Specialized Visualizations
  • 3.6 Summary: Major Visualizations and Operations, by Data Mining Goal
  • Problems
  • Chapter 4: Dimension Reduction
  • 4.1 Introduction
  • 4.2 Curse of Dimensionality
  • 4.3 Practical Considerations
  • 4.4 Data Summaries
  • 4.5 Correlation Analysis
  • 4.6 Reducing the Number of Categories in Categorical Variables
  • 4.7 Converting a Categorical Variable to a Numerical Variable
  • 4.8 Principal Components Analysis
  • 4.9 Dimension Reduction Using Regression Models
  • 4.10 Dimension Reduction Using Classification and Regression Trees
  • Problems
  • Part III: Performance Evaluation
  • Chapter 5: Evaluating Predictive Performance
  • 5.1 Introduction
  • 5.2 Evaluating Predictive Performance
  • 5.3 Judging Classifier Performance
  • 5.4 Judging Ranking Performance
  • 5.5 Oversampling
  • Problems
  • Part IV: Prediction and Classification Methods
  • Chapter 6: Multiple Linear Regression
  • 6.1 Introduction
  • 6.2 Explanatory vs. Predictive Modeling
  • 6.3 Estimating the Regression Equation and Prediction
  • 6.4 Variable Selection in Linear Regression
  • Problems
  • Chapter 7: k-Nearest-Neighbors (k-NN)
  • 7.1 The 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: The Naive Bayes Classifier
  • 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
  • 9.3 Evaluating the Performance of a Classification Tree
  • 9.4 Avoiding Overfitting
  • 9.5 Classification Rules from Trees
  • 9.6 Classification Trees for More Than two Classes
  • 9.7 Regression Trees
  • 9.8 Advantages, Weaknesses, and Extensions
  • 9.9 Improving Prediction: Multiple Trees
  • Problems
  • Chapter 10: Logistic Regression
  • 10.1 Introduction

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