Mahout介绍

浅浅的花香味﹌ 2022-03-16 06:28 185阅读 0赞

# **What is Mahout, why need it?** #

Mahout 是机器学习和数据挖掘的一个分布式框架，区别于其他的开源数据挖掘软件，它是基于hadoop之上的； 所以Mahout的优势就是hadoop的优势。官网[http://mahout.apache.org/][http_mahout.apache.org] 上说的Scalable就是指hadoop的可扩展性。

Mahout用map-reduce实现了部分数据挖掘算法，解决了并行挖掘的问题。这里说的“解决”是一个初步的概念，很多算法由于各种原因是无法用map-reduce并行实现的。

# Supported Algorithms #

**Classification**

[Logistic Regression ][Logistic Regression](SGD)

[Bayesian ][Bayesian]

[Support Vector Machines ][Support Vector Machines](SVM)

[Perceptron and Winnow][]

[Neural Network][]

[Random Forests][]

[Restricted Boltzmann Machines][]

[Online Passive Aggressive][]

[Boosting][]

[Hidden Markov Models][]

**Clustering**

[Canopy Clustering][]

[K-Means Clustering][]

[Fuzzy K-Means][]

[Expectation Maximization][] (EM)

[Mean Shift Clustering][]

[Hierarchical Clustering][]

[Dirichlet Process Clustering][]

[Latent Dirichlet Allocation][]

[Spectral Clustering][]

[Minhash Clustering][]

[Top Down Clustering][]

**Pattern Mining**

[Parallel FP Growth Algorithm][]

**Dimension reduction**

[Singular Value Decomposition and other Dimension Reduction Techniques][]

[Stochastic Singular Value Decomposition with PCA workflow][]

[Principal Components Analysis][]

[Independent Component Analysis][]

[Gaussian Discriminative Analysis][]

**Recommenders / Collaborative Filtering**

[Non-distributed recommenders ("Taste") ][Non-distributed recommenders _Taste_]

[Distributed Item-Based Collaborative Filtering ][Distributed Item-Based Collaborative Filtering]

[Collaborative Filtering using a parallel matrix factorization ][Collaborative Filtering using a parallel matrix factorization]

# **Install and use** #

下载Mahout([http://www.apache.org/dyn/closer.cgi/mahout/][http_www.apache.org_dyn_closer.cgi_mahout] ), 解压；

在MAHOUT\_HOME/bin目录下，在mahout中添加:

export JAVA\_HOME=XXXX

export HADOOP\_HOME=XXXX

export HADOOP\_CONF\_DIR=XXXX

HADOOP\_CONF\_DIR如果没设置，会默认为HADOOP\_HOME/conf

编译：MAHOUT\_HOME目录下，执行mvn clean && mvn compile && mvn -DskipTests install

好了， 提示SUCCESS则OK

how to use

在MAHOUT\_HOME/bin目录下，执行./mahout --help 可以看到mahout目前拥有的算法

## ![点击查看原始大小图片][34929156-c3b7-3075-a9e1-81e88fcde151.png] ##

也可以在MAHOUT\_HOME/src/conf/driver.classes.props文件中查看各个算法的入口，如果要添加新的算法，也可以在这个文件中注册。

## ![点击查看原始大小图片][4c1185c5-f86e-3a1a-a450-8553bd5f27b3.png] ##

算法的执行:

例如执行贝叶斯分类 (训练过程)

MAHOUT\_HOME/bin目录下 执行./mahout trainclassifier -h 查看参数

## ![09562803-2a74-3090-b2af-94c479979cba.png][] ##

# reference #

参考[https://cwiki.apache.org/MAHOUT/quickstart.html][https_cwiki.apache.org_MAHOUT_quickstart.html]

[http_mahout.apache.org]: http://mahout.apache.org/
[Logistic Regression]: https://cwiki.apache.org/confluence/display/MAHOUT/Logistic+Regression
[Bayesian]: https://cwiki.apache.org/confluence/display/MAHOUT/Bayesian
[Support Vector Machines]: https://cwiki.apache.org/confluence/display/MAHOUT/Support+Vector+Machines
[Perceptron and Winnow]: https://cwiki.apache.org/confluence/display/MAHOUT/Perceptron+and+Winnow
[Neural Network]: https://cwiki.apache.org/confluence/display/MAHOUT/Neural+Network
[Random Forests]: https://cwiki.apache.org/confluence/display/MAHOUT/Random+Forests
[Restricted Boltzmann Machines]: https://cwiki.apache.org/confluence/display/MAHOUT/Restricted+Boltzmann+Machines
[Online Passive Aggressive]: https://cwiki.apache.org/confluence/display/MAHOUT/Online+Passive+Aggressive
[Boosting]: https://cwiki.apache.org/confluence/display/MAHOUT/Boosting
[Hidden Markov Models]: https://cwiki.apache.org/confluence/display/MAHOUT/Hidden+Markov+Models
[Canopy Clustering]: https://cwiki.apache.org/confluence/display/MAHOUT/Canopy+Clustering
[K-Means Clustering]: https://cwiki.apache.org/confluence/display/MAHOUT/K-Means+Clustering
[Fuzzy K-Means]: https://cwiki.apache.org/confluence/display/MAHOUT/Fuzzy+K-Means
[Expectation Maximization]: https://cwiki.apache.org/confluence/display/MAHOUT/Expectation+Maximization
[Mean Shift Clustering]: https://cwiki.apache.org/confluence/display/MAHOUT/Mean+Shift+Clustering
[Hierarchical Clustering]: https://cwiki.apache.org/confluence/display/MAHOUT/Hierarchical+Clustering
[Dirichlet Process Clustering]: https://cwiki.apache.org/confluence/display/MAHOUT/Dirichlet+Process+Clustering
[Latent Dirichlet Allocation]: https://cwiki.apache.org/confluence/display/MAHOUT/Latent+Dirichlet+Allocation
[Spectral Clustering]: https://cwiki.apache.org/confluence/display/MAHOUT/Spectral+Clustering
[Minhash Clustering]: https://cwiki.apache.org/confluence/display/MAHOUT/Minhash+Clustering
[Top Down Clustering]: https://cwiki.apache.org/confluence/display/MAHOUT/Top+Down+Clustering
[Parallel FP Growth Algorithm]: https://cwiki.apache.org/confluence/display/MAHOUT/Parallel+Frequent+Pattern+Mining
[Singular Value Decomposition and other Dimension Reduction Techniques]: https://cwiki.apache.org/confluence/display/MAHOUT/Dimensional+Reduction
[Stochastic Singular Value Decomposition with PCA workflow]: https://cwiki.apache.org/confluence/display/MAHOUT/Stochastic+Singular+Value+Decomposition
[Principal Components Analysis]: https://cwiki.apache.org/confluence/display/MAHOUT/Principal+Components+Analysis
[Independent Component Analysis]: https://cwiki.apache.org/confluence/display/MAHOUT/Independent+Component+Analysis
[Gaussian Discriminative Analysis]: https://cwiki.apache.org/confluence/display/MAHOUT/Gaussian+Discriminative+Analysis
[Non-distributed recommenders _Taste_]: https://cwiki.apache.org/confluence/display/MAHOUT/Recommender+Documentation
[Distributed Item-Based Collaborative Filtering]: https://cwiki.apache.org/confluence/display/MAHOUT/Itembased+Collaborative+Filtering
[Collaborative Filtering using a parallel matrix factorization]: https://cwiki.apache.org/confluence/display/MAHOUT/Collaborative+Filtering+with+ALS-WR
[http_www.apache.org_dyn_closer.cgi_mahout]: http://www.apache.org/dyn/closer.cgi/mahout/
[34929156-c3b7-3075-a9e1-81e88fcde151.png]: /images/20220316/8918bc2502b94ea2ac1322471309ba84.png
[4c1185c5-f86e-3a1a-a450-8553bd5f27b3.png]: /images/20220316/7c0da427d5bf41a98756d7dbf18d84a4.png
[09562803-2a74-3090-b2af-94c479979cba.png]: /images/20220316/3b7875829ddf4dc884adc7d9aa2cda7e.png
[https_cwiki.apache.org_MAHOUT_quickstart.html]: https://cwiki.apache.org/MAHOUT/quickstart.html