Learn R Programming

fdm2id (version 1.0.1)

Data Mining and R Programming for Beginners

Description

Contains functions to simplify the use of data mining methods (classification, regression, clustering, etc.), for students and beginners in R programming. Various R packages are used and wrappers are built around the main functions, to standardize the use of data mining methods (input/output): it brings a certain loss of flexibility, but also a gain of simplicity. The package name came from the French "Fouille de Données en Master 2 Informatique Décisionnelle".

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Version

Install

install.packages('fdm2id')

Monthly Downloads

360

Version

1.0.1

License

GPL-3

Maintainer

Alexandre Blansch<c3><a9>

Last Published

September 4th, 2026

Functions in fdm2id (1.0.1)

MeanShift method

Multiple Correspondence Analysis (MCA)

Linear Regression

Classification using Linear Discriminant Analysis

Kernel Regression

Classification using Logistic Regression

GRADIENTBOOSTING

Classification using Gradient Boosting

Hierarchical Cluster Analysis method

Classification using k-NN

Principal Component Analysis (PCA)

Multi-Layer Perceptron Regression

Classification using Multilayer Perceptron

Self-Organizing Maps clustering method

Clustering using K-medoids (PAM)

Classification using Random Forest

Classification using Quadratic Discriminant Analysis

Polynomial Regression

Classification using Support Vector Machine with a linear kernel

Classification using Support Vector Machine with a radial kernel

Regression using Support Vector Machine with a radial kernel

Regression using Support Vector Machine with a linear kernel

Regression using Support Vector Machine

Non-negative Matrix Factorization

Classification using Naive Bayes

Spectral clustering method

Classification using one-level decision tree

Classification using Support Vector Machine

Duplicate and add noise to a dataset

Singular Value Decomposition

Auto MPG dataset

CART information

Number of Leafs

Capitals dataset

Sample of car accident location in the UK during year 2014.

t-distributed Stochastic Neighbor Embedding

Alcohol dataset

Check and clean class labels

APRIORI classification model

Canonical Disciminant Analysis model

Number of Nodes

Shared documentation for the 'average' and 'positive' parameters

Comparison of two sets of clusters, using kappa

Plot the Cook's distance of a linear regression model

Cookies dataset

compare.jaccard

Comparison of two sets of clusters, using Jaccard index

compare.accuracy

Comparison of two sets of clusters, using accuracy

Boosting methods model

"data1" dataset

Confusion matrix

"data2" dataset

Comparison of two sets of clusters

Population and location of 18 major british cities.

Flea beetles dataset

Close a graphics device

Clustering Box Plots

Correlated variables

Plot Cost Curves

Target1 dataset

Parabol dataset

Two moons dataset

Gaussian mixture dataset

Eucalyptus dataset

"data3" dataset

evaluation.adjr2

Adjusted R2 evaluation of regression predictions

evaluation.accuracy

Accuracy of classification predictions

Expectation-Maximization model

Plot a k-distance graphic

Target2 dataset

Training set and test set

evaluation.precision

Precision of classification predictions

evaluation.fmeasure

Decathlon dataset

evaluation.fowlkesmallows

Fowlkes–Mallows index

evaluation.recall

Recall of classification predictions

Open a graphics device

Filtering a set of rules

factorial-class

Factorial analysis results

R2 evaluation of regression predictions

Ionosphere dataset

Clustering evaluation through Dunn's index

Evaluation of classification or regression predictions

evaluation.kappa

Kappa evaluation of classification predictions

evaluation.msep

MSEP evaluation of regression predictions

evaluation.goodness

Remove redundancy in a set of rules

evaluation.jaccard

intern.intraclass

Clustering evaluation through intraclass inertia

meanshift-class

MeanShift model

intern.interclass

Clustering evaluation through interclass inertia

Extract words and phrases from a corpus

Clustering evaluation through internal criteria

K Nearest Neighbours model

Plot the leverage points of a linear regression model

load a text file

Estimation of the number of clusters for K-means

Generic classification or regression model

Plot function for som-class

Learning Parameters

Plot actual vs. predictions

Performance estimation

Plot a feature selection

Plot function for factorial-class

Predict function for EM

predict.factorial

Projection of new observations into a factorial space

Predict function for K-means

Predict function for hierarchical clustering

Model predictions

Predict function for DBSCAN

Generic Plot Method for Clustering

Plot word cloud

Predict function for a self-organising map

predict.selection

Model predictions

Print an ensemble model

Print a classification model obtained by APRIORI

Predict function for PAM

Model predictions

Plot function for apriori-class

predict.spectral

Predict function for Spectral clustering

Print a canonical discriminant analysis

predict.textmining

Model predictions

Plot function for cda-class

predict.boosting

Model predictions

predict.apriori

Model predictions

Print tuned method parameters

print.selection

Print a feature selection result

print.meanshift

Print a mean shift clustering

Plot rank versus frequency

Advanced plot function

predict.meanshift

Predict function for MeanShift

Model predictions

Print a classification or regression model

Print a K-nearest-neighbours model

print.factorial

Plot function for factorial-class

Print a DBSCAN clustering

Plot the studentized residuals of a linear regression model

Print an EM clustering

Plot function for a regression model

Print a spectral clustering

Print a self-organising map

Print a training/test split

selection-class

Feature selection

Plot ROC Curves

Clustering Scatter Plots

Feature selection for classification

Dendrogram Plots

exportgraphics.off

Toggle graphic exports

Spectral clustering model

vectorizer-class

Document vectorization object

Self-Organizing Maps model

Summary of a classification or regression model

textmining-class

Text mining object

Titanic dataset

Document vectorization

Temperature dataset

vectorize.words

Word vectorization

University dataset

Splits a dataset into training set and test set

Shared documentation of the arguments every learning method takes

Clustering evaluation through stability

summary.apriori

Print summary of a classification model obtained by APRIORI

Classification using AdaBoost

Classification using APRIORI

Expectation-Maximization clustering method

DBSCAN clustering method

Regression using Gradient Boosting

Correspondence Analysis (CA)

Classification using Canonical Discriminant Analysis

FEATURESELECTION

Classification with Feature selection

Classification using CART

Classification using Bagging