DATA MINING
Desktop Survival Guide
by
Graham Williams
Desktop Survival
Project Home
Preface
List of Figures
List of Tables
Process
.
Data
Graphics in R
Understanding Data
Preparing Data
Building Models
Evaluating Models
Algorithms
Apriori
Bagging
Bayes Classifier
Boosting
Conditional Trees
Decision Trees
Hierarchical Clustering
Kernel Methods
K-Means
K-Nearest Neighbours
Linear Models
Logistic Regression
Neural Networks
Support Vector Machines
Text Mining
Open Products
Borgelt Data Mining Suite
R
Rattle
Weka
Closed Products
C4.5
Clementine
Enterprise Miner
Equbits Foresight
GhostMiner
InductionEngine
ODM
Statistica Data Miner
TreeNet
Virtual Predict
Appendicies
Glossary
Bibliography
Index
Algorithms
Subsections
Apriori
Summary
Overview
Algorithm
Examples
Video Marketing: Transactions From File
Survey Data: Data Preparation
Other Examples
Resources and Further Reading
Bagging
Summary
Overview
Example
Algorithm
Resources and Further Reading
Bayes Classifier
Summary
Example
Algorithm
Resources and Further Reading
Boosting
Summary
Overview
AdaBoost Algorithm
Examples
Step by Step
Using gbm
Resources and Further Reading
Bootstrapping
Summary
Usage
Further Information
Conditional Trees
Summary
Algorithm
Examples
Resources and Further Reading
Decision Trees
Summary
Algorithm
Examples
Simple Example
Convert Tree to Rules
Predicting Wine Type
Predicting Salary Group
Predicting Fraud: Underrepresented Classes
Alternatives and Enhancements
Resources and Further Reading
Hierarchical Clustering
Summary
Examples
Resources and Further Reading
Kernel Methods
K-Means
Summary
Clusters
Basic Clustering
Hot Spots
Alternative Clustering
K-Nearest Neighbours
Summary
Resources and Further Reading
Linear Models
Linear Model
Logistic Regression
Summary
Resources and Further Reading
Neural Networks
Overview
Algorithm
Neural Network
Resources and Further Reading
Random Forests
Resources and Further Reading
SVM
Overview
Examples
Resources and Further Reading
Text Mining
Text Mining with R
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