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
Data
Subsections
Data Manipulation in R
Data Types
Numbers
Strings
Building Strings
Splitting Strings
Substitution
Trim Whitespace
Evaluating Strings
Logical
Vectors
Arrays
Lists
Sets
Matricies
Data Frames
Accessing Columns
Time and Dates
Space
General Manipulation
Factors
Elements
Rows and Columns
Finding Index of Elements
Partitions
Head and Tail
Reverse a List
Sorting
Unique Values
Saving and Loading
R
Data and Objects
Formatted Output
Automatically Generate Filenames
Obtaining Data
Reading Data
Vector Data
R Datasets
The Iris Dataset
CSV Data
The Wine Dataset
The Cardiac Arrhythmia Dataset
The Adult Survey Dataset
Using SQLite
ODBC Data
Database Connection
Excel
Access
Clipboard Data
Map Data
Other Data Formats
Fixed Width Data
Global Positioning System
Documenting a Dataset
Common Data Problems
Copyright © 2004-2006 Graham.Williams@togaware.com
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