Mahout Learning mahout Introduction, installation, configuration, entry program testing

I. Introduction of Mahout

Check the Chinese meaning of mahout--the people, and then look at Mahout logo, well, want to play with the small yellow elephant happy, have to accompany the person to accompany the man to play a trick ...

Attached Logo:

(That's him, the mahout on the Elephant's head)

Step into the text:

Mahout is a powerful data mining tool that is a collection of distributed machine learning algorithms, including: implementation, classification, clustering of distributed collaborative filtering called taste. Mahout The biggest advantage is based on Hadoop implementation, a lot of previously run on a single-machine algorithm, converted to MapReduce mode, which greatly improved the algorithm can handle the amount of data and processing performance. machine learning algorithms implemented in Mahout:

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
Mining Association Rules	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 algorithms	Parallelization of the 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

Second, mahout installation, configuration

First, download Mahouthttp://archive.apache.org/dist/mahout/ Second, decompressionTAR-ZXVF mahout-distribution-0.9.tar.gz Third, configure environment variables3.1. Configure MAHOUT environment variable # set Mahout environmentexport mahout_home=/home/yujianxin/mahout/mahout-distribution-0.9export mahout_conf_dir= $MAHOUT _home/confexport path= $MAHOUT _home/conf: $MAHOUT _home/bin: $PATH 3.2, configure the required Hadoop environment variables for MAHOUT # Set Hadoop environmentexport hadoop_home=/home/yujianxin/hadoop/hadoop-1.1.2
Export hadoop_conf_dir= $HADOOP _home/conf
Export path= $PATH: $HADOOP _home/binexport hadoop_home_warn_suppress=not_null   Iv. Verifying that the mahout is installed successfullyExecutes the command mahout. If you list some algorithms, you are successful, v. Use of mahout for entry-level use5.1, start Hadoop5.2, download test data http://archive.ics.uci.edu/ml/databases/synthetic_control/link in the Synthetic_control.data 5.3,  Uploading test data Hadoop fs-put synthetic_control.data/user/root/testdata5.4 uses the Kmeans clustering algorithm in Mahout to execute commands: Mahout-core Org.apache.mahout.clustering.syntheticcontrol.kmeans.Job takes about 9 minutes to complete clustering.  5.5 View clustering results perform Hadoop fs-ls/user/root/output to view clustering results. To live, to work. Mahout continue to learn ...

Mahout Learning mahout Introduction, installation, configuration, entry program testing