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fdm2id (version 0.9.5)

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

439

Version

0.9.5

License

GPL-3

Maintainer

Alexandre Blansch<c3><a9>

Last Published

January 4th, 2021

Functions in fdm2id (0.9.5)

Classification using Bagging

Expectation-Maximization clustering method

FEATURESELECTION

Classification with Feature selection

DBSCAN clustering method

Classification using CART

Classification using Canonical Discriminant Analysis

Classification using APRIORI

Correspondence Analysis (CA)

Classification using AdaBoost

GRADIENTBOOSTING

Classification using Gradient Boosting

Hierarchical Cluster Analysis method

Spectral clustering method

Kernel Regression

Classification using Naive Bayes

MeanShift method

Boosting methods model

Classification using k-NN

Multi-Layer Perceptron Regression

Classification using Linear Discriminant Analysis

Classification using Logistic Regression

Classification using Random Forest

Singular Value Decomposition

Polynomial Regression

Classification using Quadratic Discriminant Analysis

Linear Regression

Classification using Support Vector Machine

Classification using Multilayer Perceptron

Auto MPG dataset

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

t-distributed Stochastic Neighbor Embedding

Canonical Disciminant Analysis model

Self-Organizing Maps clustering method

Classification using Support Vector Machine with a linear kernel

Multiple Correspondence Analysis (MCA)

Non-negative Matrix Factorization

CART information

Plot Cost Curves

Comparison of two sets of clusters

Close a graphics device

Correlated variables

Principal Component Analysis (PCA)

Classification using Support Vector Machine with a radial kernel

"data1" dataset

Classification using one-level decision tree

Flea beetles dataset

evaluation.accuracy

Accuracy of classification predictions

Plot the Cook's distance of a linear regression model

Cookies dataset

"data2" dataset

Eucalyptus dataset

Regression using Support Vector Machine with a radial kernel

Regression using Support Vector Machine

evaluation.fmeasure

Alcohol dataset

Regression using Support Vector Machine with a linear kernel

APRIORI classification model

"data3" dataset

Evaluation of classification or regression predictions

Comparison of two sets of clusters, using kappa

Filtering a set of rules

compare.accuracy

Comparison of two sets of clusters, using accuracy

compare.jaccard

Comparison of two sets of clusters, using Jaccard index

load a text file

Training set and test set

Confuion matrix

Clustering Box Plots

Population and location of 18 major british cities.

meanshift-class

MeanShift model

Plot function for som-class

Plot function for factorial-class

predict.apriori

Model predictions

Plot a k-distance graphic

Gaussian mixture dataset

predict.boosting

Model predictions

Expectation-Maximization model

factorial-class

Factorial analysis results

Open a graphics device

Plot the leverage points of a linear regression model

Parabol dataset

Two moons dataset

evaluation.msep

MSEP evaluation of regression predictions

Model predictions

predict.meanshift

Predict function for MeanShift

Print a classification model obtained by APRIORI

predict.textmining

Model predictions

Self-Organizing Maps model

evaluation.jaccard

evaluation.kappa

Kappa evaluation of classification predictions

Number of Leafs

Temperature dataset

Number of Nodes

summary.apriori

Print summary of a classification model obtained by APRIORI

Target1 dataset

Ionosphere dataset

Target2 dataset

Remove redundancy in a set of rules

Extract words and phrases from a corpus

Estimation of the number of clusters for K-means

K Nearest Neighbours model

Clustering evaluation through Dunn's index

Clustering evaluation through internal criteria

evaluation.precision

Precision of classification predictions

Generic Plot Method for Clustering

Plot word cloud

Advanced plot function

Performance estimation

Plot function for cda-class

Decathlon dataset

vectorizer-class

Document vectorization object

print.factorial

Plot function for factorial-class

vectorize.words

Word vectorization

Plot rank versus frequency

Plot the studentized residuals of a linear regression model

Plot function for a regression model

Generic classification or regression model

evaluation.goodness

evaluation.fowlkesmallows

Fowlkes<U+2013>Mallows index

selection-class

Feature selection

exportgraphics.off

Toggle graphic exports

Feature selection for classification

Predict function for DBSCAN

Model predictions

R2 evaluation of regression predictions

Dendrogram Plots

University dataset

evaluation.recall

Recall of classification predictions

Document vectorization

Clustering evaluation through stability

Splits a dataset into training set and test set

intern.interclass

Clustering evaluation through interclass inertia

Predict function for EM

intern.intraclass

Clustering evaluation through intraclass inertia

Predict function for K-means

Learning Parameters

Model predictions

predict.selection

Model predictions

Plot ROC Curves

textmining-class

Text mining object

Titanic dataset

Spectral clustering model

Clustering Scatter Plots