Making sense of data II a practical guide to data visualization, advanced data mining methods, and applications
A hands-on guide to making valuable decisions from data using advanced data mining methods and techniques This second installment in the Making Sense of Data series continues to explore a diverse range of commonly used approaches to making and communicating decisions from data. Delving into more tec...
| Autor principal: | |
|---|---|
| Otros Autores: | |
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Hoboken, N.J. :
John Wiley & Sons
c2009.
|
| Edición: | 1st edition |
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009627632606719 |
Tabla de Contenidos:
- MAKING SENSE OF DATA II; CONTENTS; PREFACE; 1 INTRODUCTION; 1.1 Overview; 1.2 Definition; 1.3 Preparation; 1.3.1 Overview; 1.3.2 Accessing Tabular Data; 1.3.3 Accessing Unstructured Data; 1.3.4 Understanding the Variables and Observations; 1.3.5 Data Cleaning; 1.3.6 Transformation; 1.3.7 Variable Reduction; 1.3.8 Segmentation; 1.3.9 Preparing Data to Apply; 1.4 Analysis; 1.4.1 Data Mining Tasks; 1.4.2 Optimization; 1.4.3 Evaluation; 1.4.4 Model Forensics; 1.5 Deployment; 1.6 Outline of Book; 1.6.1 Overview; 1.6.2 Data Visualization; 1.6.3 Clustering; 1.6.4 Predictive Analytics
- 1.6.5 Applications1.6.6 Software; 1.7 Summary; 1.8 Further Reading; 2 DATA VISUALIZATION; 2.1 Overview; 2.2 Visualization Design Principles; 2.2.1 General Principles; 2.2.2 Graphics Design; 2.2.3 Anatomy of a Graph; 2.3 Tables; 2.3.1 Simple Tables; 2.3.2 Summary Tables; 2.3.3 Two-Way Contingency Tables; 2.3.4 Supertables; 2.4 Univariate Data Visualization; 2.4.1 Bar Chart; 2.4.2 Histograms; 2.4.3 Frequency Polygram; 2.4.4 Box Plots; 2.4.5 Dot Plot; 2.4.6 Stem-and-Leaf Plot; 2.4.7 Quantile Plot; 2.4.8 Quantile-Quantile Plot; 2.5 Bivariate Data Visualization; 2.5.1 Scatterplot
- 2.6 Multivariate Data Visualization2.6.1 Histogram Matrix; 2.6.2 Scatterplot Matrix; 2.6.3 Multiple Box Plot; 2.6.4 Trellis Plot; 2.7 Visualizing Groups; 2.7.1 Dendrograms; 2.7.2 Decision Trees; 2.7.3 Cluster Image Maps; 2.8 Dynamic Techniques; 2.8.1 Overview; 2.8.2 Data Brushing; 2.8.3 Nearness Selection; 2.8.4 Sorting and Rearranging; 2.8.5 Searching and Filtering; 2.9 Summary; 2.10 Further Reading; 3 CLUSTERING; 3.1 Overview; 3.2 Distance Measures; 3.2.1 Overview; 3.2.2 Numeric Distance Measures; 3.2.3 Binary Distance Measures; 3.2.4 Mixed Variables; 3.2.5 Other Measures
- 3.3 Agglomerative Hierarchical Clustering3.3.1 Overview; 3.3.2 Single Linkage; 3.3.3 Complete Linkage; 3.3.4 Average Linkage; 3.3.5 Other Methods; 3.3.6 Selecting Groups; 3.4 Partitioned-Based Clustering; 3.4.1 Overview; 3.4.2 k-Means; 3.4.3 Worked Example; 3.4.4 Miscellaneous Partitioned-Based Clustering; 3.5 Fuzzy Clustering; 3.5.1 Overview; 3.5.2 Fuzzy k-Means; 3.5.3 Worked Examples; 3.6 Summary; 3.7 Further Reading; 4 PREDICTIVE ANALYTICS; 4.1 Overview; 4.1.1 Predictive Modeling; 4.1.2 Testing Model Accuracy; 4.1.3 Evaluating Regression Models' Predictive Accuracy
- 4.1.4 Evaluating Classification Models' Predictive Accuracy4.1.5 Evaluating Binary Models' Predictive Accuracy; 4.1.6 ROC Charts; 4.1.7 Lift Chart; 4.2 Principal Component Analysis; 4.2.1 Overview; 4.2.2 Principal Components; 4.2.3 Generating Principal Components; 4.2.4 Interpretation of Principal Components; 4.3 Multiple Linear Regression; 4.3.1 Overview; 4.3.2 Generating Models; 4.3.3 Prediction; 4.3.4 Analysis of Residuals; 4.3.5 Standard Error; 4.3.6 Coefficient of Multiple Determination; 4.3.7 Testing the Model Significance; 4.3.8 Selecting and Transforming Variables
- 4.4 Discriminant Analysis
