The algorithms we list here are used for classification, clustering, statistical learning, association analysis and link mining. The list gives you an overview of what’s available. 

Algorithm/method Description
Regression
(Predict/classify)
Prediction and classification: linear and logistic are common
Penalised regression
(Predict/classify)
Prediction and classification using reduced set of variables
Ridge regression
(Prediction)
Form of penalised regression, dimension reduction
Lasso regression
(Prediction)
Form of penalised regression, dimension reduction
Partial least squares
(Prediction)
Form of penalised regression, dimension reduction
Naïve Bayes
(Predict/classify)
Create a score to predict/classify using input data and prior
Bayesian networks
(Predict/classify)
Probabilistic network model
Neural networks
(Predict/classify)
Form layers of nonlinear functions of input variables
CART, C4.5, C5.0
(Classification)
Recursively split data into increasingly smaller subgroups
Random forest, GBM
(Classification)
Other types of tree-based approaches like CART
Apriori
(Classification)
Find association rules from frequent sets of variables or items
SVM
(Classification)
Find linear function of inputs that separates the classes
kNN
(Classification)
Predict class based on majority vote of k nearest neighbours
AdaBoost
(Classification)
Use multiple algorithms, ensemble learning method
PageRank
(Pattern finding)
Find associations (rank websites) based on links (hyperlinks)
Mixtures
(Density estimation)
Describe non-standard densities, also used to find clusters
EM
(Clustering)
Estimate clusters as mixture of normal distributions
K-Means
(Clustering)
Allocates points to closest cluster based on distance measure
PCA, FA
(Dimension reduction)
Convert input variables to smaller set of output variables

Do you have a reference where these or other algorithms are explained well?

sasindia

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