# Data Mining

* Data Mining

# Data Mining with Rattle

* Introduction
* Data
* Select Data
* Explore
* Transform
* Classify
* Unsupervised
* Text Mining
* Evaluation and Deployment
* Issues
* Moving into R
* Troubleshooting

# R for the Data Miner

* R: The Language
* Data
* Graphics in R
* Understanding Data
* Preparing Data
* Descriptive and Predictive Analytics
* Cluster Analysis: K-Means
* Association Analysis: Apriori
* Classification: Decision Trees
* Classification: Boosting
* Classification: Random Forests
* Issues
* Evaluating Models
* Cluster Analysis

# Algorithms

* Bagging: Meta Algorithm
* Bayes Classifier: Classification
* Bootstrapping: Meta Algorithm
* Cluster Analysis
* Conditional Trees: Classification
* Hierarchical Clustering: Clustering
* K-Nearest Neighbours: Classification
* Linear Models
* Regression: Ordinal Regression
* Regression: Logistic Regression
* Neural Networks: Classification and Regression
* Support Vector Machines: Classification

# Open Products

* AlphaMiner
* Borgelt Data Mining Suite: From University of Magdeburg
* KNime
* R: From the R Foundation
* Rattle: From Togaware
* Weka: From University of Waikato

# Closed Products

* C4.5: Classification
* Clementine: From SPSS
* Equbits Foresight: Tool from Equbits
* GhostMiner: From Fujitsu
* InductionEngine: Tool from PredictionWorks
* ODM: Oracle Data Mining
* Enterprise Miner: From SAS
* Statistica Data Miner: From StatSoft
* TreeNet: From Salford Systems
* Virtual Predict: From Virtual Genetics

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