Visual Data Mining The VisMiner Approach.

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Detalles Bibliográficos
Autor principal: Anderson, Russell K.
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Newark : John Wiley & Sons, Incorporated, 2012.
Colección:New York Academy of Sciences Ser.
Temas:
Electronic books.
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Visual Data Mining: THE VISMINER APPROACH
  • Contents
  • Preface
  • Acknowledgments
  • 1. Introduction
  • Data Mining Objectives
  • Introduction to VisMiner
  • The Data Mining Process
  • Initial Data Exploration
  • Dataset Preparation
  • Algorithm Selection and Application
  • Model Evaluation
  • Summary
  • 2. Initial Data Exploration and Dataset Preparation Using VisMiner
  • The Rationale for Visualizations
  • Tutorial
  • Using VisMiner
  • Initializing VisMiner
  • Initializing the Slave Computers
  • Opening a Dataset
  • Viewing Summary Statistics
  • Exercise 2.1
  • The Correlation Matrix
  • Exercise 2.2
  • The Histogram
  • The Scatter Plot
  • Exercise 2.3
  • The Parallel Coordinate Plot
  • Exercise 2.4
  • Extracting Sub-populations Using the Parallel Coordinate Plot
  • Exercise 2.5
  • The Table Viewer
  • The Boundary Data Viewer
  • Exercise 2.6
  • The Boundary Data Viewer with Temporal Data
  • Exercise 2.7
  • Summary
  • 3. Advanced Topics in Initial Exploration and Dataset Preparation Using VisMiner
  • Missing Values
  • Missing Values
  • An Example
  • Exploration Using the Location Plot
  • Exercise 3.1
  • Dataset Preparation
  • Creating Computed Columns
  • Exercise 3.2
  • Aggregating Data for Observation Reduction
  • Exercise 3.3
  • Combining Datasets
  • Exercise 3.4
  • Outliers and Data Validation
  • Range Checks
  • Fixed Range Outliers
  • Distribution Based Outliers
  • Computed Checks
  • Exercise 3.5
  • Feasibility and Consistency Checks
  • Data Correction Outside of VisMiner
  • Distribution Consistency
  • Pattern Checks
  • A Pattern Check of Experimental Data
  • Exercise 3.6
  • Summary
  • 4. Prediction Algorithms for Data Mining
  • Decision Trees
  • Stopping the Splitting Process
  • A Decision Tree Example
  • Using Decision Trees
  • Decision Tree Advantages
  • Limitations
  • Artificial Neural Networks
  • Overfitting the Model
  • Moving Beyond Local Optima
  • ANN Advantages and Limitations
  • Support Vector Machines
  • Data Transformations
  • Moving Beyond Two-dimensional Predictors
  • SVM Advantages and Limitations
  • Summary
  • 5. Classification Models in VisMiner
  • Dataset Preparation
  • Tutorial
  • Building and Evaluating Classification Models
  • Model Evaluation
  • Exercise 5.1
  • Prediction Likelihoods
  • Classification Model Performance
  • Interpreting the ROC Curve
  • Classification Ensembles
  • Model Application
  • Summary
  • Exercise 5.2
  • Exercise 5.3
  • 6. Regression Analysis
  • The Regression Model
  • Correlation and Causation
  • Algorithms for Regression Analysis
  • Assessing Regression Model Performance
  • Model Validity
  • Looking Beyond R2
  • Polynomial Regression
  • Artificial Neural Networks for Regression Analysis
  • Dataset Preparation
  • Tutorial
  • A Regression Model for Home Appraisal
  • Modeling with the Right Set of Observations
  • Exercise 6.1
  • ANN Modeling
  • The Advantage of ANN Regression
  • Top-Down Attribute Selection
  • Issues in Model Interpretation
  • Model Validation
  • Model Application
  • Summary

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