DATA MINING
Desktop Survival Guide
by
Graham Williams
Desktop Survival
Project Home
List of Figures
List of Tables
Data Mining
Data Mining
Data Mining with Rattle
Introduction
Data
Transform
Explore
A Model Building Framework
Unsupervised Modelling
Two Class Models
Multi Class Models
Regression Models
Text Mining
Evaluation and Deployment
Moving into R
Troubleshooting
R for the Data Miner
R
Data
Graphics in R
Understanding Data
Preparing Data
Building Models
Evaluating Models
Algorithms
Apriori
Bagging
Bayes Classifier
Boosting
Cluster Analysis
Conditional Trees
Hierarchical Clustering
K-Means
K-Nearest Neighbours
Linear Models
Logistic Regression
Neural Networks
Support Vector Machines
Text Mining
Open Products
AlphaMiner
Borgelt Data Mining Suite
KNime
R
Rattle
Weka
Closed Products
C4.5
Clementine
Equbits Foresight
GhostMiner
InductionEngine
ODM
Enterprise Miner
Statistica Data Miner
TreeNet
Virtual Predict
Appendicies
Glossary
Bibliography
Index
Closed Products
Subsections
C4.5
Summary
Overview
Example
Algorithm
Resources and Further Reading
Clementine
Summary
Equbits Foresight
Summary
GhostMiner
Summary
Usage
InductionEngine
Summary
Oracle Data Mining
Summary
Usage
SAS Enterprise Miner
Summary
Usage
Tips and Tricks
Statistica
Summary
Usage
Sample Applications
Further Information
TreeNet
Summary
Virtual Predict
Summary
Usage
Copyright © 2004-2006 Graham.Williams@togaware.com
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