R: Data Mining and R Programming for Beginners

# Data Mining and R Programming for Beginners

## Documentation for package ‘fdm2id’ version 0.9.9

- DESCRIPTION file

## Help Pages

A B C D E F G H I K L M N O P Q R S T U V W Z

## -- A --

accident2014 | Sample of car accident location in the UK during year 2014.
ADABOOST | Classification using AdaBoost
alcohol | Alcohol dataset
APRIORI | Classification using APRIORI
apriori-class | APRIORI classification model
augmentation | Duplicate and add noise to a dataset
autompg | Auto MPG dataset

## -- B --

BAGGING | Classification using Bagging
beetles | Flea beetles dataset
birth | Birth dataset
boosting-class | Boosting methods model
boxclus | Clustering Box Plots
britpop | Population and location of 18 major british cities.

## -- C --

CA | Correspondence Analysis (CA)
CART | Classification using CART
cartdepth | Depth
cartinfo | CART information
cartleafs | Number of Leafs
cartnodes | Number of Nodes
cartplot | CART Plot
CDA | Classification using Canonical Discriminant Analysis
cda-class | Canonical Disciminant Analysis model
closegraphics | Close a graphics device
compare | Comparison of two sets of clusters
compare.accuracy | Comparison of two sets of clusters, using accuracy
compare.jaccard | Comparison of two sets of clusters, using Jaccard index
compare.kappa | Comparison of two sets of clusters, using kappa
confusion | Confuion matrix
cookies | Cookies dataset
cookies.desc.test | Cookies dataset
cookies.desc.train | Cookies dataset
cookies.y.test | Cookies dataset
cookies.y.train | Cookies dataset
cookplot | Plot the Cook's distance of a linear regression model
correlated | Correlated variables
cost.curves | Plot Cost Curves
credit | Credit dataset

## -- D --

data.diag | Square dataset
data.gauss | Gaussian mixture dataset
data.parabol | Parabol dataset
data.target1 | Target1 dataset
data.target2 | Target2 dataset
data.twomoons | Two moons dataset
data.xor | XOR dataset
data1 | "data1" dataset
data2 | "data2" dataset
data3 | "data3" dataset
dataset-class | Training set and test set
dbs-class | DBSCAN model
DBSCAN | DBSCAN clustering method
decathlon | Decathlon dataset
distplot | Plot a k-distance graphic

## -- E --

EM | Expectation-Maximization clustering method
em-class | Expectation-Maximization model
eucalyptus | Eucalyptus dataset
evaluation | Evaluation of classification or regression predictions
evaluation.accuracy | Accuracy of classification predictions
evaluation.adjr2 | Adjusted R2 evaluation of regression predictions
evaluation.fmeasure | F-measure
evaluation.fowlkesmallows | Fowlkes–Mallows index
evaluation.goodness | Goodness
evaluation.jaccard | Jaccard index
evaluation.kappa | Kappa evaluation of classification predictions
evaluation.msep | MSEP evaluation of regression predictions
evaluation.precision | Precision of classification predictions
evaluation.r2 | R2 evaluation of regression predictions
evaluation.recall | Recall of classification predictions
exportgraphics | Open a graphics device
exportgraphics.off | Toggle graphic exports
exportgraphics.on | Toggle graphic exports

## -- F --

factorial-class | Factorial analysis results
FEATURESELECTION | Classification with Feature selection
filter.rules | Filtering a set of rules
frequentwords | Frequent words

## -- G --

general.rules | Remove redundancy in a set of rules
getvocab | Extract words and phrases from a corpus
GRADIENTBOOSTING | Classification using Gradient Boosting

## -- H --

HCA | Hierarchical Cluster Analysis method

## -- I --

intern | Clustering evaluation through internal criteria
intern.dunn | Clustering evaluation through Dunn's index
intern.interclass | Clustering evaluation through interclass inertia
intern.intraclass | Clustering evaluation through intraclass inertia
ionosphere | Ionosphere dataset

## -- K --

kaiser | Kaiser rule
KERREG | Kernel Regression
KMEANS | K-means method
kmeans.getk | Estimation of the number of clusters for _K_-means
KNN | Classification using k-NN
knn-class | K Nearest Neighbours model

## -- L --

LDA | Classification using Linear Discriminant Analysis
leverageplot | Plot the leverage points of a linear regression model
LINREG | Linear Regression
linsep | Linsep dataset
loadtext | load a text file
LR | Classification using Logistic Regression

## -- M --

MCA | Multiple Correspondence Analysis (MCA)
MEANSHIFT | MeanShift method
meanshift-class | MeanShift model
MLP | Classification using Multilayer Perceptron
MLPREG | Multi-Layer Perceptron Regression
model-class | Generic classification or regression model
movies | Movies dataset

## -- N --

NB | Classification using Naive Bayes
NMF | Non-negative Matrix Factorization

## -- O --

ozone | Ozone dataset

## -- P --

params-class | Learning Parameters
PCA | Principal Component Analysis (PCA)
performance | Performance estimation
plot.cda | Plot function for cda-class
plot.factorial | Plot function for factorial-class
plot.som | Plot function for som-class
plotavsp | Plot actual vs. predictions
plotcloud | Plot word cloud
plotclus | Generic Plot Method for Clustering
plotdata | Advanced plot function
plotzipf | Plot rank versus frequency
POLYREG | Polynomial Regression
predict.apriori | Model predictions
predict.boosting | Model predictions
predict.cda | Model predictions
predict.dbs | Predict function for DBSCAN
predict.em | Predict function for EM
predict.kmeans | Predict function for K-means
predict.knn | Model predictions
predict.meanshift | Predict function for MeanShift
predict.model | Model predictions
predict.selection | Model predictions
predict.textmining | Model predictions
print.apriori | Print a classification model obtained by APRIORI
print.factorial | Plot function for factorial-class
pseudoF | Pseudo-F

## -- Q --

QDA | Classification using Quadratic Discriminant Analysis
query.docs | Document query
query.words | Word query

## -- R --

RANDOMFOREST | Classification using Random Forest
reg1 | reg1 dataset
reg1.test | reg1 dataset
reg1.train | reg1 dataset
reg2 | reg2 dataset
reg2.test | reg2 dataset
reg2.train | reg2 dataset
regplot | Plot function for a regression model
resplot | Plot the studentized residuals of a linear regression model
roc.curves | Plot ROC Curves
rotation | Rotation
runningtime | Running time

## -- S --

scatterplot | Clustering Scatter Plots
selectfeatures | Feature selection for classification
selection-class | Feature selection
snore | Snore dataset
SOM | Self-Organizing Maps clustering method
som-class | Self-Organizing Maps model
SPECTRAL | Spectral clustering method
spectral-class | Spectral clustering model
spine | Spine dataset
spine.test | Spine dataset
spine.train | Spine dataset
splitdata | Splits a dataset into training set and test set
stability | Clustering evaluation through stability
STUMP | Classification using one-level decision tree
summary.apriori | Print summary of a classification model obtained by APRIORI
SVD | Singular Value Decomposition
SVM | Classification using Support Vector Machine
SVMl | Classification using Support Vector Machine with a linear kernel
SVMr | Classification using Support Vector Machine with a radial kernel
SVR | Regression using Support Vector Machine
SVRl | Regression using Support Vector Machine with a linear kernel
SVRr | Regression using Support Vector Machine with a radial kernel

## -- T --

temperature | Temperature dataset
TEXTMINING | Text mining
textmining-class | Text mining object
titanic | Titanic dataset
toggleexport | Toggle graphic exports
toggleexport.off | Toggle graphic exports
toggleexport.on | Toggle graphic exports
treeplot | Dendrogram Plots
TSNE | t-distributed Stochastic Neighbor Embedding

## -- U --

universite | University dataset

## -- V --

vectorize.docs | Document vectorization
vectorize.words | Word vectorization
vectorizer-class | Document vectorization object
vowels | Vowels dataset
vowels.test | Vowels dataset
vowels.train | Vowels dataset

## -- W --

wheat | Wheat dataset
wine | Wine dataset

## -- Z --

zoo | Zoo dataset