Common data Mining algorithm packages in R

Data mining is divided into 4 categories, that is, prediction, classification, clustering and association, according to different mining purposes to select the corresponding algorithm. Here is a summary of the data mining packages commonly used in the R language:

Prediction of continuous dependent variables:

Stats-Packet lm function for multivariate linear regression

Stats-Packet glm function for generalized linear regression

Stats packet nls function to realize nonlinear least squares regression

Rpart packet Rpart function, a classification regression tree model based on cart algorithm

Rweka packet m5p function, model tree algorithm, advantages of set linear regression and cart algorithm

Adabag packet bagging function, an integrated algorithm based on Rpart algorithm

Adabag packet boosting function, an integrated algorithm based on Rpart algorithm

Randomforest packet randomforest function, an integrated algorithm based on Rpart algorithm

e1071 packet SVM function, support vector machine algorithm

Kernlab packet ksvm function, support vector machine based on kernel function

Nnet packet nnet function, a single hidden layer neural network algorithm

Neuralnet packet neuralnet function, multiple hidden layer multi-node neural network algorithm

Rsnns Packet MLP function, multilayer perceptron neural network

Rsnns packet RBF function, neural network based on radial basis function

Classification of discrete dependent variables:

Stats package GLM function, implement logistic regression, select Logit connection function

Stats packet knn function, k nearest neighbor algorithm

KKNN packet kknn function, weighted k nearest neighbor algorithm

Rpart packet Rpart function, a classification regression tree model based on cart algorithm

Adabag packet bagging function, an integrated algorithm based on Rpart algorithm

Adabag packet boosting function, an integrated algorithm based on Rpart algorithm

Randomforest packet randomforest function, an integrated algorithm based on Rpart algorithm

Party Package Ctree function, conditional classification tree algorithm

Rweka packet Oner function, one-dimensional learning rule algorithm

Rweka packet Jpip function, multi-dimensional learning rule algorithm

Rweka packet J48 function, decision tree based on C4.5 algorithm

C50 packet C5.0 function, decision tree based on C5.0 algorithm

e1071 packet SVM function, support vector machine algorithm

Kernlab packet ksvm function, support vector machine based on kernel function

e1071 packet naivebayes function, Bayesian classifier algorithm

Klar packet naivebayes function, Bayesian classifier calculation

Mass packet lda function, linear discriminant analysis

Mass packet Qda function, two-time discriminant analysis

Nnet packet nnet function, a single hidden layer neural network algorithm

Rsnns Packet MLP function, multilayer perceptron neural network

Rsnns packet RBF function, neural network based on radial basis function

Cluster Type:

The Nbclust packet nbclust function determines how many classes should be clustered

Stats packet Kmeans function, K-mean clustering algorithm

Cluster packet Pam function, K-centric point clustering algorithm

Stats packet hclust function, hierarchical clustering algorithm

FPC packet Dbscan function, density clustering algorithm

FPC package kmeansruns function, compared to the Kmeans function more stable, but also can be estimated to several types of

FPC package PAMK function, compared to the PAM function, can give a reference to the number of clusters

Mclust packet mclust function, desired maximum (EM) algorithm

Association rules:

Arules packet apriori function, apriori Association rule algorithm

Common data Mining algorithm packages in R