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https://issues.apache.org/jira/browse/MAHOUT-1451?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13932879#comment-13932879
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Gaurav Misra commented on MAHOUT-1451:
--------------------------------------
Updated the description with cleaned up version of:
https://mahout.apache.org/users/clustering/clustering-of-synthetic-control-data.html
Changes:
- Some rewording/capitalization/spelling/reorganization.
- Fixed broken links, updated old links
- It seems like we don't have examples for dirichlet process clustering or
meanshift clustering but they were still listed on the page so I removed those
references.
> Cleaning up the examples for clustering on the website
> ------------------------------------------------------
>
> Key: MAHOUT-1451
> URL: https://issues.apache.org/jira/browse/MAHOUT-1451
> Project: Mahout
> Issue Type: Improvement
> Reporter: Gaurav Misra
> Original Estimate: 48h
> Remaining Estimate: 48h
>
> Cleaning up the following clustering examples:
> =====================================
> https://mahout.apache.org/users/clustering/clustering-of-synthetic-control-data.html
> Introduction
> This example will demonstrate clustering of time series data, specifically
> control charts. [Control charts : http://en.wikipedia.org/wiki/Control_chart]
> are tools used to determine whether a manufacturing or business process is in
> a state of statistical control. Such control charts are generated / simulated
> repeatedly at equal time intervals. A simulated dataset is available for use
> in UCI machine learning repository. The data is described [here :
> http://archive.ics.uci.edu/ml/databases/synthetic_control/synthetic_control.data.html].
> Problem Description
> A time series of control charts needs to be clustered into their close knit
> groups. The data set we use is synthetic and is meant to resemble real world
> information in an anonymized format. It contains six different classes:
> Normal, Cyclic, Increasing trend, Decreasing trend, Upward shift, Downward
> shift. In this example we will use Mahout to cluster the data into
> corresponding class buckets.
> At the end of this example
> * You will have clustered data using mahout.
> * You will see how to analyse the clusters produced by mahout.
> * You will have a starting point for incorporating clustering into your
> own software.
> Setup
> We need to do some initial setup before we are able to run the example.
> 1. Start out by downloading the input dataset (to be clustered) from the
> UCI Machine Learning Repository:
> http://archive.ics.uci.edu/ml/databases/synthetic_control/synthetic_control.data
> 2. Make sure the data consists of 600 rows and 60 columns. The first 100
> rows contains Normal data followed by 100 rows of Cyclic data and so on with
> a total of 6 classes.
> 3. This example assumes that you have already set up Mahout/Hadoop. If you
> have not done so yet:
> 4.
> * Hadoop: Follow the instructions on
> http://hadoop.apache.org/docs/stable/hadoop-project-dist/hadoop-common/SingleNodeSetup.html
> to set up Hadoop.
> * Mahout: Follow the instructions on the [Quickstart:
> https://mahout.apache.org/users/basics/quickstart.html] page.
> 5. Make sure the Hadoop daemons are running if you are running Hadoop in
> distributed mode.
> 6. Create a directory on your local machine called « testdata » and place
> the input dataset in this directory.
> 7. Run the following command to copy the input data into HDFS:
> * Create a directory called « testdata » on HDFS:
> $HADOOP_HOME/bin/hadoop fs -mkdir testdata
> * Copy the directory named « testdata » from your local filesystem to
> HDFS:
> $HADOOP_HOME/bin/hadoop fs -put testdata
> 8. The final setup step is to build Mahout by going to the $MAHOUT_HOME
> directory and running one of the following commands:
> 9.
> * For a full build: mvn clean install
> * For a build without unit tests: mvn -DskipTests clean install
> 10. You should see a build successful message once the build script has
> completed.
> 11. Finally make sure that the examples have compiled successfully. You
> should find the compiled jar in the /examples/target directory under the name
> mahout-examples-{version}.job.jar
> 12. This concludes all the setup required to run the examples.
> Clustering Examples
> There are examples available for three clustering algorithms:
> * Canopy Clustering:
> https://mahout.apache.org/users/clustering/canopy-clustering.html
> * k-Means Clustering:
> https://mahout.apache.org/users/clustering/k-means-clustering.html
> * Fuzzy k-Means Clustering:
> https://mahout.apache.org/users/clustering/fuzzy-k-means.html
> Depending on the example you want to run the following command can be used:
> * Canopy Clustering: $MAHOUT_HOME/bin/mahout
> org.apache.mahout.clustering.syntheticcontrol.canopy.Job
> * k-Means Clustering: $MAHOUT_HOME/bin/mahout
> org.apache.mahout.clustering.syntheticcontrol.kmeans.Job
> * Fuzzy k-Means Clustering: $MAHOUT_HOME/bin/mahout
> org.apache.mahout.clustering.syntheticcontrol.fuzzykmeans.Job
> The clustering output will be produced in the « output » directory on HDFS.
> The output should be copied to your local filesystem since it is overwritten
> on each run.
> Use the following command to copy out the data to your local filesystem:
> $HADOOP_HOME/bin/hadoop fs -get output $MAHOUT_HOME/examples
> This creates an output folder inside examples directory. The output data
> points are in vector format. In order to read/analyze the output, you can use
> [clusterdump: https://mahout.apache.org/users/clustering/cluster-dumper.html]
> utility provided by Mahout.
> The source code for these examples is located under the examples project.
> =====================================
> https://mahout.apache.org/users/clustering/clustering-seinfeld-episodes.html
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