【前言】
Mahout 是一个很强大的数据挖掘工具，是一个分布式机器学习算法的集合，包括：被称为Taste的分布式协同过滤的实现、分类、聚类等。Mahout最大的优点就是基于hadoop实现，把很多以前运行于单机上的算法，转化为了MapReduce模式，这样大大提升了算法可处理的数据量和处理性能。
最主要的是mahout提供了很多机器学习的算法
Paste_Image.png
【安装部署】
注意：mahout的运行方式有两种，一种是local本地运行，一种是结合hadoop运行，这里主要记录mahout在hadoop上运行
#下载源码
http://archive.apache.org/dist/mahout/
#解压
tar -xzvf mahout-distribution-0.9.tar.gz
#配置内容
--java配置
export JAVA_HOME="/opt/jdk1.7.0_79"
--hadoop配置（这里是hadoop1.x的配置）
export HADOOP_HOME_WARN_SUPPRESS="1"
export HADOOP_HOME=/usr/local/hadoop
export HADOOP_CONF_DIR=$HADOOP_HOME/conf
export MAHOUT_HOME="/usr/local/mahout-0.9"
export MAHOUT_CONF_DIR="$MAHOUT_HOME/conf"
export MAHOUT_LOCAL=""
export PATH=".:$HADOOP_HOME/bin:$MAHOUT_HOME/conf:$MAHOUT_HOME/bin:$JAVA_HOME/bin:$PATH"
export CLASSPATH=.:$JAVA_HOME/lib/dt.jar:$MAHOUT_HOME/lib:$HADOOP_CONF_DIR/:$JAVA_HOME/lib/tools.jar
#启动hadoop
./start-all.sh
#验证mahout
mahout
#启动日志
[root@hadoop Desktop]# mahout
MAHOUT_LOCAL is not set; adding HADOOP_CONF_DIR to classpath.
Running on hadoop, using /usr/local/hadoop/bin/hadoop and HADOOP_CONF_DIR=/usr/local/hadoop/conf
MAHOUT-JOB: /usr/local/mahout-0.9/mahout-examples-0.9-job.jar
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 to {SequenceFile, SequenceFile}
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
[root@hadoop Desktop]#
以上表名启动成功
【运行kmeans算法案例】
#测试数据准备
下载地址：http://archive.ics.uci.edu/ml/databases/synthetic_control/synthetic_control.data
#hdfs上创建测试目录（目录名必须是testdata）
hadoop fs -mkdir ./testdata 或者 hadoop fs -mkdir /user/root/testdata（即本用户目录下，这里是root用户）
#上传测试数据
hadoop fs -put /mahoutTestData.txt ./ 或者 hadoop fs -put /mahoutTestData.txt /user/root/testdata
#运行
hadoop jar mahout-examples-0.9-job.jar org.apache.mahout.clustering.syntheticcontrol.kmeans.Job
#查看结果
hadoop fs -ls ./output
#我们会发现结果是乱码的（mahout转码打开）
mahout vectordump -i ./output/data/part-m-00000
上传后的测试数据源.png
![运行完成.png . . .]
](http://upload-images.jianshu.io/upload_images/2608446-12a1737d2093633e.png?imageMogr2/auto-orient/strip%7CimageView2/2/w/1240)
结果列表.png
计算后的结果源.png