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fdm2id (version 0.9.1)

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 Donnes en Master 2 Informatique Dcisionnelle".

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Version

Install

install.packages('fdm2id')

Monthly Downloads

377

Version

0.9.1

License

GPL-3

Maintainer

Alexandre Blansch<c3><a9>

Last Published

January 10th, 2020

Functions in fdm2id (0.9.1)

Classification using AdaBoost

Classification using APRIORI

DBSCAN clustering method

Classification using Bagging

FEATURESELECTION

Classification with Feature selection

Hierarchical Cluster Analysis method

Classification using Canonical Discriminant Analysis

Classification using CART

GRADIENTBOOSTING

Classification using Gradient Boosting

Expectation-Maximization clustering method

Classification using k-NN

Kernel Regression

Classification using Logistic Regression

Linear Regression

Regression using Support Vector Machine

Singular Value Decomposition

Classification using one-level decision tree

Classification using Naive Bayes

Multi-Layer Perceptron Regression

Classification using Support Vector Machine with a radial kernel

MeanShift method

Classification using Support Vector Machine

Classification using Multilayer Perceptron

Classification using Support Vector Machine with a linear kernel

APRIORI classification model

Classification using Quadratic Discriminant Analysis

Auto MPG dataset

Classification using Random Forest

Bootstrap evaluation

Boosting methods model

Classification using Linear Discriminant Analysis

Comparison of two sets of clusters

Non-negative Matrix Factorization

compare.accuracy

Comparison of two sets of clusters, using accuracy

Regression using Support Vector Machine with a linear kernel

Self-Organizing Maps clustering method

Spectral clustering method

Number of Leafs

Regression using Support Vector Machine with a radial kernel

CART information

bootstrap.curves

Plot evaluation curves with bootstrap sampling

t-distributed Stochastic Neighbor Embedding

Flea beetles dataset

Plot Cost Curves

Clustering Box Plots

compare.jaccard

Comparison of two sets of clusters, using Jaccard index

Plot a k-distance graphic

Expectation-Maximization model

Comparison of two sets of clusters, using kappa

evaluation.accuracy

Accuracy of classification predictions

Close a graphics device

Canonical Disciminant Analysis model

Target1 dataset

evaluation.recall

Recall of classification predictions

Open a graphics device

Evaluation of classification or regression predictions

Parabol dataset

Filtering a set of rules

meanshift-class

MeanShift model

evaluation.goodness

evaluation.jaccard

Estimation of the number of clusters for K-means

Ionosphere dataset

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

Polynomial Regression

Alcohol dataset

Generic classification or regression model

Target2 dataset

Plot the Cook's distance of a linear regression model

Cookies dataset

"data2" dataset

"data1" dataset

Population and location of 18 major british cities.

Number of Nodes

Two moons dataset

Decathlon dataset

Eucalyptus dataset

Plot the studentized residuals of a linear regression model

Advanced plot function

Model predictions

Generic Plot Method for Clustering

predict.meanshift

Predict function for MeanShift

Plot function for a regression model

Gaussian mixture dataset

"data3" dataset

Training set and test set

Evaluate several classication (or regression) methods

Predict function for EM

Predict function for DBSCAN

evaluation.kappa

Kappa evaluation of classification predictions

evaluation.msep

MSEP evaluation of regression predictions

Clustering evaluation through Dunn's index

Clustering evaluation through internal criteria

evaluation.fmeasure

Titanic dataset

textmining-class

Text mining object

evaluation.precision

Precision of classification predictions

Learning Parameters

R2 evaluation of regression predictions

intern.intraclass

Clustering evaluation through intraclass inertia

Plot function for cda-class

intern.interclass

Clustering evaluation through interclass inertia

vectorize.words

Word vectorization

Remove redundancy in a set of rules

predict.boosting

Model predictions

evaluation.fowlkesmallows

Fowlkes<U+2013>Mallows index

Predict function for K-means

vectorizer-class

Document vectorization object

Model predictions

Plot the leverage points of a linear regression model

K Nearest Neighbours model

Plot word cloud

Plot function for som-class

Print a classification model obtained by APRIORI

Model predictions

Feature selection for classification

Spectral clustering model

selection-class

Feature selection

Clustering Scatter Plots

summary.apriori

Print summary of a classification model obtained by APRIORI

Splits a dataset into training set and test set

Plot ROC Curves

Clustering evaluation through stability

Temperature dataset

Document vectorization

University dataset

Plot rank versus frequency

load a text file

Extract words and phrases from a corpus

predict.apriori

Model predictions

predict.selection

Model predictions

predict.textmining

Model predictions

exportgraphics.off

Toggle graphic exports

Dendrogram Plots

Self-Organizing Maps model