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
Summary
Complexity:
Clustering is usually expensive and K-Means is
.
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