Data Mining and R Programming for Beginners

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 |

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. |

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 |

cookplot | Plot the Cook's distance of a linear regression model |

correlated | Correlated variables |

cost.curves | Plot Cost Curves |

credit | Credit dataset |

data1 | "data1" dataset |

data2 | "data2" dataset |

data3 | "data3" dataset |

data.diag | Square dataset |

data.gauss | Gaussian mixture dataset |

data.parabol | Parabol dataset |

dataset-class | Training set and test set |

data.target1 | Target1 dataset |

data.target2 | Target2 dataset |

data.twomoons | Two moons dataset |

data.xor | XOR dataset |

DBSCAN | DBSCAN clustering method |

dbs-class | DBSCAN model |

decathlon | Decathlon dataset |

distplot | Plot a k-distance graphic |

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 | Fowlkesâ€“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 |

factorial-class | Factorial analysis results |

FEATURESELECTION | Classification with Feature selection |

filter.rules | Filtering a set of rules |

frequentwords | Frequent words |

general.rules | Remove redundancy in a set of rules |

getvocab | Extract words and phrases from a corpus |

GRADIENTBOOSTING | Classification using Gradient Boosting |

HCA | Hierarchical Cluster Analysis method |

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 |

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 |

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 |

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 |

NB | Classification using Naive Bayes |

NMF | Non-negative Matrix Factorization |

ozone | Ozone dataset |

params-class | Learning Parameters |

PCA | Principal Component Analysis (PCA) |

performance | Performance estimation |

plotavsp | Plot actual vs. predictions |

plot.cda | Plot function for cda-class |

plotcloud | Plot word cloud |

plotclus | Generic Plot Method for Clustering |

plotdata | Advanced plot function |

plot.factorial | Plot function for factorial-class |

plot.som | Plot function for som-class |

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 |

QDA | Classification using Quadratic Discriminant Analysis |

query.docs | Document query |

query.words | Word query |

RANDOMFOREST | Classification using Random Forest |

reg1 | reg1 dataset |

reg2 | 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 |

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 |

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... |

SVMr | Classification using Support Vector Machine with a radial... |

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 |

temperature | Temperature dataset |

TEXTMINING | Text mining |

textmining-class | Text mining object |

titanic | Titanic dataset |

toggleexport | Toggle graphic exports |

treeplot | Dendrogram Plots |

TSNE | t-distributed Stochastic Neighbor Embedding |

universite | University dataset |

vectorize.docs | Document vectorization |

vectorizer-class | Document vectorization object |

vectorize.words | Word vectorization |

vowels | Vowels dataset |

wheat | Wheat dataset |

wine | Wine dataset |

zoo | Zoo dataset |

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