Learn R Programming

fdm2id (version 0.9.9)

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

439

Version

0.9.9

License

GPL-3

Maintainer

Alexandre Blansch<c3><a9>

Last Published

June 12th, 2023

Functions in fdm2id (0.9.9)

DBSCAN clustering method

Expectation-Maximization clustering method

Classification using APRIORI

Classification using Logistic Regression

Multiple Correspondence Analysis (MCA)

MeanShift method

Classification using Multilayer Perceptron

Linear Regression

Non-negative Matrix Factorization

Classification using Linear Discriminant Analysis

Hierarchical Cluster Analysis method

Kernel Regression

Classification using one-level decision tree

Spectral clustering method

Classification using Support Vector Machine

Polynomial Regression

Classification using Random Forest

Singular Value Decomposition

Classification using k-NN

Principal Component Analysis (PCA)

Boosting methods model

Population and location of 18 major british cities.

Clustering Box Plots

Regression using Support Vector Machine

Classification using Quadratic Discriminant Analysis

Classification using Support Vector Machine with a linear kernel

Classification using Support Vector Machine with a radial kernel

Target1 dataset

Parabol dataset

evaluation.recall

Recall of classification predictions

"data1" dataset

R2 evaluation of regression predictions

Regression using Support Vector Machine with a linear kernel

compare.jaccard

Comparison of two sets of clusters, using Jaccard index

Comparison of two sets of clusters, using kappa

APRIORI classification model

Alcohol dataset

Plot the Cook's distance of a linear regression model

Number of Nodes

Correlated variables

Self-Organizing Maps clustering method

"data3" dataset

Evaluation of classification or regression predictions

"data2" dataset

evaluation.accuracy

Accuracy of classification predictions

Target2 dataset

Two moons dataset

Expectation-Maximization model

Eucalyptus dataset

Estimation of the number of clusters for K-means

Open a graphics device

factorial-class

Factorial analysis results

Multi-Layer Perceptron Regression

Classification using Naive Bayes

CART information

Regression using Support Vector Machine with a radial kernel

evaluation.msep

MSEP evaluation of regression predictions

evaluation.precision

Precision of classification predictions

Clustering evaluation through internal criteria

Generic classification or regression model

Clustering evaluation through Dunn's index

Number of Leafs

t-distributed Stochastic Neighbor Embedding

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

K Nearest Neighbours model

Performance estimation

Flea beetles dataset

evaluation.adjr2

Adjusted R2 evaluation of regression predictions

Gaussian mixture dataset

evaluation.fmeasure

Learning Parameters

Duplicate and add noise to a dataset

load a text file

meanshift-class

MeanShift model

Plot the leverage points of a linear regression model

Plot word cloud

Print a classification model obtained by APRIORI

Plot actual vs. predictions

predict.boosting

Model predictions

Plot function for cda-class

Plot the studentized residuals of a linear regression model

Plot ROC Curves

print.factorial

Plot function for factorial-class

Titanic dataset

Auto MPG dataset

Model predictions

predict.meanshift

Predict function for MeanShift

predict.selection

Model predictions

Model predictions

predict.textmining

Model predictions

exportgraphics.off

Toggle graphic exports

Canonical Disciminant Analysis model

Plot Cost Curves

Close a graphics device

Splits a dataset into training set and test set

Training set and test set

Plot rank versus frequency

vectorizer-class

Document vectorization object

Comparison of two sets of clusters

evaluation.jaccard

predict.apriori

Model predictions

Clustering Scatter Plots

Feature selection for classification

Cookies dataset

Confuion matrix

compare.accuracy

Comparison of two sets of clusters, using accuracy

Decathlon dataset

Spectral clustering model

Self-Organizing Maps model

Dendrogram Plots

evaluation.kappa

Kappa evaluation of classification predictions

Remove redundancy in a set of rules

University dataset

Plot a k-distance graphic

selection-class

Feature selection

Ionosphere dataset

evaluation.fowlkesmallows

Fowlkes–Mallows index

Filtering a set of rules

evaluation.goodness

Generic Plot Method for Clustering

Advanced plot function

Model predictions

Predict function for K-means

Extract words and phrases from a corpus

Plot function for a regression model

Clustering evaluation through stability

intern.intraclass

Clustering evaluation through intraclass inertia

intern.interclass

Clustering evaluation through interclass inertia

Plot function for factorial-class

Plot function for som-class

Predict function for EM

Predict function for DBSCAN

summary.apriori

Print summary of a classification model obtained by APRIORI

Temperature dataset

textmining-class

Text mining object

Document vectorization

vectorize.words

Word vectorization

Classification using Bagging

FEATURESELECTION

Classification with Feature selection

Correspondence Analysis (CA)

GRADIENTBOOSTING

Classification using Gradient Boosting

Classification using CART

Classification using AdaBoost

Classification using Canonical Discriminant Analysis