Making Sense of Data II: A Practical Guide to Data Visualization, Advanced Data Mining Methods, and Applications

by Glenn J. Myatt (Leadscope, Inc. ); Wayne P. Johnson (Leadscope, Inc. )

• ISBN13: 9780470417393
• ISBN10: 0470417390
• Format: eBook
• Copyright: 2009-03-01
• Publisher: Wiley

Summary

The Making Sense of Data series fills a current gap in the market for easy-to-use books for non-specialists that combine advanced data mining methods, the application of these methods to a range of fields, and hands-on tutorials. Making Sense of Data II: A Practical Guide to Data Visualization, Advanced Data Mining Methods, and Applications offers a comprehensive collection of advanced data mining methods coupled with tutorials for applications in a range of fields including business and finance. This book is appropriate for students and professionals in the many different disciplines involving making decisions from data.

Table of Contents

Preface

Introduction.

Overview

Definition

Preparation

Overview

Accessing Tabular Data

Accessing Unstructured Data

Understanding the Variables and Observations

Data Cleaning

Transformation

Variable Reduction

Segmentation

Preparing Data to Apply

Analysis

Data Mining Tasks

Optimization

Evaluation

Model Forensics

Deployment

Outline of Book

Overview

Data Visualization

Clustering

Predictive Analytics

Applications

Software

Summary

Further Reading

Data Visualization.

Overview

Visualization Design Principles

General Principles

Graphics Design

Anatomy of a Graph

Tables

Simple Tables

Summary Tables

Two-Way Contingency Tables

Supertables

Univariate Data Visualization

Bar Chart

Histograms

Frequency Polygram

Box Plots

Dot Plot

Stem-and-Leaf Plot

Quantile Plot

Quantile-Quantile Plot

Bivariate Data Visualization

Scatterplot

Multivariate Data Visualization

Histogram Matrix

Scatterplot Matrix

Multiple Box Plot

Trellis Plot

Visualizing Groups

Dendrograms

Decision Trees

Cluster Image Maps

Dynamic Techniques

Overview

Data Brushing

Nearness Selection

Sorting and Rearranging

Searching and Filtering

Summary

Further Reading

Clustering.

Overview

Distance Measures

Overview

Numeric Distance Measures

Binary Distance Measures

Mixed Variables

Others Measures

Agglomerative Hierarchical Clustering

Overview

Single Linkage

Complete Linkage

Average Linkage

Other Methods

Selecting Groups

Partitioned-Based Clustering

Overview

k-Means

Worked Example

Miscellaneous Partitioned-Based Clustering

Fuzzy Clustering

Overview

Fuzzy k-Means

Worked Examples

Summary

Further Reading

Predictive Analytics.

Overview

Predictive Modeling

Testing Model Accuracy

Evaluating Regression ModelsÆ Predictive Accuracy

Evaluating Classification ModelsÆ Predictive Accuracy

Evaluating Binary ModelsÆ Predictive Accuracy

ROC Charts

Lift Chart

Principal Component Analysis

Overview

Principal Components

Generating Principal Components

Interpretation of Principal Components

Multiple Linear Regression

Overview

Generating Models

Prediction

Analysis of Residuals

Standard Error

Coefficient of Multiple Determination

Testing the Model Significance

Selecting and Transforming Variables

Discriminant Analysis

Overview

Discriminant Function

Discriminant Analysis Example

Logistic Regression

Overview

Logistic Regression Formula

Estimating Coefficients

Assessing and Optimizing Results

Naive Bayes Classifiers

Overview

Bayes Theorem and the Independence Assumption

Independence Assumption

Classification Process

Summary

Further Reading

Applications.

Overview

Sales and Marketing

Industry-Specific Data Mining

Finance

Insurance

Retail

Telecommunications

Manufacturing

Entertainment

Government

Pharmaceuticals

Healthcare

MicroRNA Data Analysis Case Study

Defining the Problem

Preparing the Data

Analysis

Credit Scoring Case Study

Defining the Problem

Preparing the Data

Analysis

Deployment

Data Mining Nontabular Data

Overview

Data Mining Chemical Data

Data Mining Text

Further Reading

Matrices

Overview of Matrices

Matrix Addition

Matrix Multiplication

Transpose of a Matrix

Inverse of a Matrix

Software

Software Overview

Software Objectives

Access and Installation

User interface Overview

Data Preparation

Overview

Reading in Data

Searching the Data

Variable Characterization

Removing Observations and Variables

Cleaning the Data

Transforming the Data

Segmentation

Principal Component Analysis

Tables and Graphs

Overview

Contingency Tables

Summary Tables

Graphs

Graph Matrices

Statistics

Overview

Descriptive Statistics

Confidence Intervals

Hypothesis Tests

Chi-square Test

ANOVA

Comparative Statistics

Grouping

Overview

Clustering

Associative Rules

Decision Trees

Prediction

Overview

Linear Regression

Discriminant Analysis

Logistic Regression

Naive Bayes

kNN

CART

Neural Networks

Apply Model

Bibliography

Index

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