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fdm2id (version 0.9.4)

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 Donnes en Master 2 Informatique Dcisionnelle".

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Version

Install

install.packages('fdm2id')

Monthly Downloads

330

Version

0.9.4

License

GPL-3

Maintainer

Alexandre Blansch<c3><a9>

Last Published

October 30th, 2020

Functions in fdm2id (0.9.4)

Correspondence Analysis (CA)

FEATURESELECTION

Classification with Feature selection

Classification using APRIORI

Classification using CART

Classification using Bagging

GRADIENTBOOSTING

Classification using Gradient Boosting

Classification using Canonical Discriminant Analysis

Classification using AdaBoost

DBSCAN clustering method

Expectation-Maximization clustering method

Singular Value Decomposition

Non-negative Matrix Factorization

Principal Component Analysis (PCA)

Classification using Support Vector Machine

Classification using k-NN

Clustering Box Plots

Number of Leafs

Population and location of 18 major british cities.

Number of Nodes

Auto MPG dataset

Kernel Regression

Hierarchical Cluster Analysis method

MeanShift method

Multi-Layer Perceptron Regression

Classification using Logistic Regression

Regression using Support Vector Machine

Classification using Naive Bayes

Regression using Support Vector Machine with a linear kernel

Multiple Correspondence Analysis (MCA)

Linear Regression

Classification using Linear Discriminant Analysis

Plot Cost Curves

Correlated variables

Classification using Multilayer Perceptron

Boosting methods model

Classification using one-level decision tree

Spectral clustering method

Regression using Support Vector Machine with a radial kernel

Classification using Support Vector Machine with a radial kernel

Classification using Support Vector Machine with a linear kernel

Classification using Random Forest

Self-Organizing Maps clustering method

Polynomial Regression

Classification using Quadratic Discriminant Analysis

Alcohol dataset

APRIORI classification model

Flea beetles dataset

Comparison of two sets of clusters, using kappa

Comparison of two sets of clusters

Cookies dataset

Close a graphics device

Gaussian mixture dataset

Parabol dataset

Confuion matrix

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

t-distributed Stochastic Neighbor Embedding

"data3" dataset

Plot the Cook's distance of a linear regression model

Training set and test set

Decathlon dataset

factorial-class

Factorial analysis results

Open a graphics device

Canonical Disciminant Analysis model

"data1" dataset

evaluation.accuracy

Accuracy of classification predictions

Extract words and phrases from a corpus

evaluation.fmeasure

Plot the leverage points of a linear regression model

Performance estimation

Remove redundancy in a set of rules

Plot a k-distance graphic

intern.interclass

Clustering evaluation through interclass inertia

Expectation-Maximization model

CART information

"data2" dataset

K Nearest Neighbours model

Evaluation of classification or regression predictions

Generic Plot Method for Clustering

Eucalyptus dataset

Estimation of the number of clusters for K-means

Plot word cloud

compare.accuracy

Comparison of two sets of clusters, using accuracy

evaluation.fowlkesmallows

Fowlkes<U+2013>Mallows index

compare.jaccard

Comparison of two sets of clusters, using Jaccard index

Predict function for K-means

Predict function for EM

Advanced plot function

Learning Parameters

intern.intraclass

Clustering evaluation through intraclass inertia

Plot the studentized residuals of a linear regression model

Plot function for a regression model

summary.apriori

Print summary of a classification model obtained by APRIORI

Plot function for cda-class

print.factorial

Plot function for factorial-class

Temperature dataset

selection-class

Feature selection

Feature selection for classification

Spectral clustering model

Two moons dataset

evaluation.jaccard

evaluation.kappa

Kappa evaluation of classification predictions

evaluation.goodness

Filtering a set of rules

predict.apriori

Model predictions

R2 evaluation of regression predictions

Ionosphere dataset

evaluation.recall

Recall of classification predictions

Target1 dataset

predict.boosting

Model predictions

Target2 dataset

Plot rank versus frequency

Print a classification model obtained by APRIORI

Self-Organizing Maps model

predict.textmining

Model predictions

University dataset

Document vectorization

exportgraphics.off

Toggle graphic exports

Dendrogram Plots

Generic classification or regression model

vectorize.words

Word vectorization

vectorizer-class

Document vectorization object

predict.selection

Model predictions

Predict function for DBSCAN

Model predictions

Model predictions

textmining-class

Text mining object

Clustering Scatter Plots

evaluation.msep

MSEP evaluation of regression predictions

evaluation.precision

Precision of classification predictions

Titanic dataset

Clustering evaluation through internal criteria

load a text file

Plot function for factorial-class

Clustering evaluation through Dunn's index

meanshift-class

MeanShift model

Model predictions

Plot function for som-class

predict.meanshift

Predict function for MeanShift

Plot ROC Curves

Clustering evaluation through stability

Splits a dataset into training set and test set