本文记录在Hadoop集群环境下安装Mahout。
环境：OS：Centos 6.5 x64 & Soft：Hadoop 1.2.1 & Mahout 0.9
1、简介
mahout项目主页：https://mahout.apache.org/
下载二进制包，上传到服务器。
2、安装
用集群环境用户安装，解压二进制包。
[huser@master hadoop]$ tar -xvf mahout-distribution-0.9.tar.gz
3、配置环境变量
[huser@master ~]$ vi /etc/profile export JAVA_HOME=/usr/java/jdk1.7.0_51 export CLASSPATH=.:$JAVA_HOME/jre/lib:$JAVA_HOME/lib/tools.jar export JRE_HOME=$JAVA_HOME/jre export HADOOP_HOME=/home/huser/hadoop/hadoop-1.2.1 export HADOOP_CONF_DIR=/home/huser/hadoop/hadoop-1.2.1/conf export HADOOP_CLASSPATH=/home/huser/hadoop/hadoop-1.2.1/bin export MAHOUT_HOME=/home/huser/hadoop/mahout-distribution-0.9 export MAHOUT_HOME_DIR=/home/huser/hadoop/mahout-distribution-0.9/conf export PATH=$PATH:$JAVA_HOME/bin:$MAHOUT_HOME/bin:$MAHOUT_HOME/conf
[root@master huser]# source /etc/profile
4、测试
[huser@master ~]$ mahout MAHOUT_LOCAL is not set; adding HADOOP_CONF_DIR to classpath. Warning: $HADOOP_HOME is deprecated. Running on hadoop, using /home/huser/hadoop/hadoop-1.2.1/bin/hadoop and HADOOP_CONF_DIR=/home/huser/hadoop/hadoop-1.2.1/conf MAHOUT-JOB: /home/huser/hadoop/mahout-distribution-0.9/mahout-examples-0.9-job.jar Warning: $HADOOP_HOME is deprecated. An example program must be given as the first argument. Valid program names are: arff.vector: : Generate Vectors from an ARFF file or directory baumwelch: : Baum-Welch algorithm for unsupervised HMM training canopy: : Canopy clustering cat: : Print a file or resource as the logistic regression models would see it cleansvd: : Cleanup and verification of SVD output clusterdump: : Dump cluster output to text clusterpp: : Groups Clustering Output In Clusters cmdump: : Dump confusion matrix in HTML or text formats concatmatrices: : Concatenates 2 matrices of same cardinality into a single matrix cvb: : LDA via Collapsed Variation Bayes (0th deriv. approx) cvb0_local: : LDA via Collapsed Variation Bayes, in memory locally. evaluateFactorization: : compute RMSE and MAE of a rating matrix factorization against probes fkmeans: : Fuzzy K-means clustering hmmpredict: : Generate random sequence of observations by given HMM itemsimilarity: : Compute the item-item-similarities for item-based collaborative filtering kmeans: : K-means clustering lucene.vector: : Generate Vectors from a Lucene index lucene2seq: : Generate Text SequenceFiles from a Lucene index matrixdump: : Dump matrix in CSV format matrixmult: : Take the product of two matrices parallelALS: : ALS-WR factorization of a rating matrix qualcluster: : Runs clustering experiments and summarizes results in a CSV recommendfactorized: : Compute recommendations using the factorization of a rating matrix recommenditembased: : Compute recommendations using item-based collaborative filtering regexconverter: : Convert text files on a per line basis based on regular expressions resplit: : Splits a set of SequenceFiles into a number of equal splits rowid: : Map SequenceFile<Text,VectorWritable> to {SequenceFile<IntWritable,VectorWritable>, SequenceFile<IntWritable,Text>} rowsimilarity: : Compute the pairwise similarities of the rows of a matrix runAdaptiveLogistic: : Score new production data using a probably trained and validated AdaptivelogisticRegression model runlogistic: : Run a logistic regression model against CSV data seq2encoded: : Encoded Sparse Vector generation from Text sequence files seq2sparse: : Sparse Vector generation from Text sequence files seqdirectory: : Generate sequence files (of Text) from a directory seqdumper: : Generic Sequence File dumper seqmailarchives: : Creates SequenceFile from a directory containing gzipped mail archives seqwiki: : Wikipedia xml dump to sequence file spectralkmeans: : Spectral k-means clustering split: : Split Input data into test and train sets splitDataset: : split a rating dataset into training and probe parts ssvd: : Stochastic SVD streamingkmeans: : Streaming k-means clustering svd: : Lanczos Singular Value Decomposition testnb: : Test the Vector-based Bayes classifier trainAdaptiveLogistic: : Train an AdaptivelogisticRegression model trainlogistic: : Train a logistic regression using stochastic gradient descent trainnb: : Train the Vector-based Bayes classifier transpose: : Take the transpose of a matrix validateAdaptiveLogistic: : Validate an AdaptivelogisticRegression model against hold-out data set vecdist: : Compute the distances between a set of Vectors (or Cluster or Canopy, they must fit in memory) and a list of Vectors vectordump: : Dump vectors from a sequence file to text viterbi: : Viterbi decoding of hidden states from given output states sequence