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fdm2id (version 0.9.6)

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

0.9.6

License

GPL-3

Maintainer

Alexandre Blansch<c3><a9>

Last Published

October 29th, 2021

Functions in fdm2id (0.9.6)

Classification using CART

Classification using Bagging

Classification using one-level decision tree

Regression using Support Vector Machine with a linear kernel

Population and location of 18 major british cities.

Regression using Support Vector Machine

Clustering Box Plots

Kernel Regression

Classification using APRIORI

Classification using Logistic Regression

Hierarchical Cluster Analysis method

Classification using AdaBoost

t-distributed Stochastic Neighbor Embedding

Correspondence Analysis (CA)

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

Comparison of two sets of clusters, using kappa

Regression using Support Vector Machine with a radial kernel

Confuion matrix

Multiple Correspondence Analysis (MCA)

Alcohol dataset

Parabol dataset

APRIORI classification model

Gaussian mixture dataset

"data1" dataset

CART information

Close a graphics device

Comparison of two sets of clusters

Principal Component Analysis (PCA)

Singular Value Decomposition

Non-negative Matrix Factorization

Cookies dataset

Plot the Cook's distance of a linear regression model

"data2" dataset

Decathlon dataset

Expectation-Maximization clustering method

Number of Leafs

DBSCAN clustering method

Eucalyptus dataset

Target1 dataset

Boosting methods model

Classification using Support Vector Machine

Canonical Disciminant Analysis model

Open a graphics device

factorial-class

Factorial analysis results

Ionosphere dataset

Number of Nodes

intern.intraclass

Clustering evaluation through intraclass inertia

intern.interclass

Clustering evaluation through interclass inertia

Filtering a set of rules

Evaluation of classification or regression predictions

Training set and test set

evaluation.msep

MSEP evaluation of regression predictions

"data3" dataset

Target2 dataset

evaluation.precision

Precision of classification predictions

Classification using k-NN

Multi-Layer Perceptron Regression

FEATURESELECTION

Classification with Feature selection

Linear Regression

Classification using Linear Discriminant Analysis

GRADIENTBOOSTING

Classification using Gradient Boosting

Classification using Naive Bayes

R2 evaluation of regression predictions

Two moons dataset

Classification using Support Vector Machine with a radial kernel

Classification using Support Vector Machine with a linear kernel

Correlated variables

Polynomial Regression

Classification using Quadratic Discriminant Analysis

compare.accuracy

Comparison of two sets of clusters, using accuracy

Flea beetles dataset

Auto MPG dataset

compare.jaccard

Comparison of two sets of clusters, using Jaccard index

Plot function for factorial-class

evaluation.recall

Recall of classification predictions

Plot word cloud

Advanced plot function

Plot function for som-class

Plot rank versus frequency

Plot a k-distance graphic

Plot Cost Curves

Model predictions

Learning Parameters

Self-Organizing Maps model

evaluation.fowlkesmallows

Fowlkes–Mallows index

Expectation-Maximization model

Generic Plot Method for Clustering

evaluation.goodness

predict.boosting

Model predictions

predict.apriori

Model predictions

Predict function for DBSCAN

predict.selection

Model predictions

Model predictions

Plot ROC Curves

Spectral clustering model

Plot the leverage points of a linear regression model

evaluation.fmeasure

evaluation.accuracy

Accuracy of classification predictions

evaluation.jaccard

Model predictions

predict.meanshift

Predict function for MeanShift

exportgraphics.off

Toggle graphic exports

Dendrogram Plots

Plot function for a regression model

Clustering evaluation through internal criteria

Plot the studentized residuals of a linear regression model

Clustering evaluation through Dunn's index

evaluation.kappa

Kappa evaluation of classification predictions

Remove redundancy in a set of rules

textmining-class

Text mining object

Titanic dataset

vectorize.words

Word vectorization

vectorizer-class

Document vectorization object

Temperature dataset

summary.apriori

Print summary of a classification model obtained by APRIORI

Extract words and phrases from a corpus

Estimation of the number of clusters for K-means

K Nearest Neighbours model

Predict function for K-means

Print a classification model obtained by APRIORI

meanshift-class

MeanShift model

Generic classification or regression model

Predict function for EM

load a text file

predict.textmining

Model predictions

Performance estimation

print.factorial

Plot function for factorial-class

Plot function for cda-class

Clustering Scatter Plots

Clustering evaluation through stability

Splits a dataset into training set and test set

Feature selection for classification

selection-class

Feature selection

University dataset

Document vectorization

Classification using Multilayer Perceptron

MeanShift method

Classification using Canonical Discriminant Analysis

Spectral clustering method

Classification using Random Forest

Self-Organizing Maps clustering method