Algorithms implemented by Mahout

The machine learning algorithms implemented in Mahout are shown in the following table

Algorithm classes	Algorithm name	Chinese name
Classification algorithm	Logistic Regression	Logistic regression
	Bayesian	Bayesian
	Svm	Support Vector Machine
	Perceptron	Perceptron algorithm
	Neural Network	Neural network
	Random forests	Random Forest
	Restricted Boltzmann Machines	Finite-Boltzmann machine
Clustering algorithm	Canopy Clustering	Canopy Clustering
	K-means Clustering	K-mean-value algorithm
	Fuzzy K-means	Fuzzy K-Mean value
	Expectation maximization	EM clustering (expected maximum clustering)
	Mean Shift Clustering	Mean Drift Clustering
	Hierarchical clustering	Hierarchical clustering
	Dirichlet Process Clustering	Dirichlet process Clustering
	Latent Dirichlet Allocation	LDA Clustering
	Spectral clustering	Spectral clustering
Association rules mining	parallel FP growth algorithm	Parallel FP growth algorithm
regression	locally Weighted Linear Regression	local weighted linear regression
dimensionality Reduction/Vieux-	Singular Value decomposition	Singular value decomposition
	Principal Components Analysis	Principal component Analysis
	Independent Component Analysis	Independent component Analysis
	Gaussian discriminative Analysis	Gaussian discriminant analysis
evolutionary algorithm	Watchmaker framework
Recommended/Collaborative filtering	Non-distributed recommenders	Taste (USERCF, ITEMCF, Slopeone)
	Distributed recommenders	Itemcf
Calculation of vector similarity	Rowsimilarityjob	Calculate the similarity between columns
	Vectordistancejob	Calculate distance between vectors
non-map-reduce algorithm	hidden Markov Models	Hidden Markov model
Collection method Extension	Collections	Extends Java's collections class

Algorithms implemented by Mahout