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R & Data Mining[edit]

R Programming
R Programming
R Basics
Introduction
Sample Session
Manage your workspace
Settings
Packages
Documentation
Control Structures
Working with functions
Debugging
Using C or Fortran
Utilities
Estimation utilities
Data Management
Data types
Working with data frames
Importing and exporting data
Text Processing
Times and Dates
Graphics
Graphics
Grammar of graphics
Publication quality output
Publication quality ouput
Descriptive Statistics
Descriptive Statistics
Mathematics
Mathematics
Optimization
Probability Distributions
Random Number Generation
Statistical Core Methods
Maximum Likelihood
Method of Moments
Bayesian Methods
Bootstrap
Multiple Imputation
Nonparametric Methods
Regression Models
Linear Models
Quantile Regression
Binomial Models
Multinomial Models
Tobit And Selection Models
Count Data Models
Duration Analysis
Time Series
Time Series
Factor Analysis
Factor Analysis
Classification
Ordination
Clustering
Network Analysis
Network Analysis
High Performance Computing
Profiling R code
Parallel computing with R
Appendix
Sources
Index
Data Mining Algorithms
Data Mining Algorithms In R
Dimensionality Reduction
Dimensionality Reduction
Principal Component Analysis
Singular Value Decomposition
Feature Selection
Frequent Pattern Mining
Frequent Pattern Mining
The Eclat Algorithm
arulesNBMiner
The Apriori Algorithm
The FP-Growth Algorithm
Sequence Mining
Sequence Mining
SPADE
DEGSeq
Clustering
Clustering
K-Means
Hybrid Hierarchical Clustering
Expectation Maximization (EM)
Dissimilarity Matrix Calculation
Hierarchical Clustering
Density-Based Clustering
K-Cores
Fuzzy Clustering - Fuzzy C-means
RockCluster
Biclust
Partitioning Around Medoids (PAM)
CLUES
Self-Organizing Maps (SOM)
Proximus
CLARA
Classification
Classification
SVM
penalizedSVM
kNN
Outliers
Decision Trees
Naïve Bayes
adaboost
JRip
R Packages
R Packages
RWeka
gausspred
optimsimplex
CCMtools
FactoMineR
nnet