You are viewing a plain text version of this content. The canonical link for it is here.
Posted to dev@mahout.apache.org by "Pavan Kumar N (JIRA)" <ji...@apache.org> on 2014/04/14 11:15:17 UTC
[jira] [Closed] (MAHOUT-1450) Cleaning up clustering documentation
on mahout website
[ https://issues.apache.org/jira/browse/MAHOUT-1450?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Pavan Kumar N closed MAHOUT-1450.
---------------------------------
> Cleaning up clustering documentation on mahout website
> -------------------------------------------------------
>
> Key: MAHOUT-1450
> URL: https://issues.apache.org/jira/browse/MAHOUT-1450
> Project: Mahout
> Issue Type: Documentation
> Components: Documentation
> Environment: This affects all mahout versions
> Reporter: Pavan Kumar N
> Labels: documentation, newbie
> Fix For: 1.0
>
>
> In canopy clustering, the strategy for parallelization seems to have some dead links. Need to clean them and replace with new links (if there are any). Here is the link:
> http://mahout.apache.org/users/clustering/canopy-clustering.html
> Here are some details of the dead links for kmeans clustering page:
> On the k-Means clustering - basics page,
> first line of the Quickstart part of the documentation, the hyperlink "Here"
> http://mahout.apache.org/users/clustering/k-means-clustering%5Equickstart-kmeans.sh.html
> first sentence of Strategy for parallelization part of documentation, the hyperlink "Cluster computing and MapReduce", second second sentence the hyperlink "here" and last sentence the hyperlink "http://www2.chass.ncsu.edu/garson/PA765/cluster.htm" are dead.
> http://code.google.com/edu/content/submissions/mapreduce-minilecture/listing.html
> http://code.google.com/edu/content/submissions/mapreduce-minilecture/lec4-clustering.ppt
> http://www2.chass.ncsu.edu/garson/PA765/cluster.htm
> Under the page: http://mahout.apache.org/users/clustering/visualizing-sample-clusters.html
> in the second sentence of Pre-prep part of this page, the hyperlink "setup mahout" is dead.
> http://mahout.apache.org/users/clustering/users/basics/quickstart.html
> The existing documentation is too ambiguous and I recommend to make the following changes so the new users can use it as tutorial.
> The Quickstart should be replaced with the following:
> Get the data from:
> wget http://www.daviddlewis.com/resources/testcollections/reuters21578/reuters21578.tar.gz
> Place it within the example folder from mahout home director:
> mahout-0.7/examples/reuters
> mkdir reuters
> cd reuters
> mkdir reuters-out
> mv reuters21578.tar.gz reuters-out
> cd reuters-out
> tar -xzvf reuters21578.tar.gz
> cd ..
> Mahout specific Commands
> #1 run the org.apache.lucene.benchmark .utils.ExtractReuters class
> ${MAHOUT_HOME}/bin/mahout
> org.apache.lucene.benchmark.utils.ExtractReuters reuters-out
> reuters-text
> #2 copy the file to your HDFS
> bin/hadoop fs -copyFromLocal
> /home/bigdata/mahout-distribution-0.7/examples/reuters-text
> hdfs://localhost:54310/user/bigdata/
> #3 generate sequence-file
> mahout seqdirectory -i hdfs://localhost:54310/user/bigdata/reuters-text
> -o hdfs://localhost:54310/user/bigdata/reuters-seqfiles -c UTF-8 -chunk 5
> -chunk → specifying the number of data blocks
> UTF-8 → specifying the appropriate input format
> #4 Check the generated sequence-file
> mahout-0.7$ ./bin/mahout seqdumper -i
> /your-hdfs-path-to/reuters-seqfiles/chunk-0 | less
> #5 From sequence-file generate vector file
> mahout seq2sparse -i
> hdfs://localhost:54310/user/bigdata/reuters-seqfiles -o
> hdfs://localhost:54310/user/bigdata/reuters-vectors -ow
> -ow → overwrite
> #6 take a look at it should have 7 items by using this command
> bin/hadoop fs -ls
> reuters-vectors/df-count
> reuters-vectors/dictionary.file-0
> reuters-vectors/frequency.file-0
> reuters-vectors/tf-vectors
> reuters-vectors/tfidf-vectors
> reuters-vectors/tokenized-documents
> reuters-vectors/wordcount
> bin/hadoop fs -ls reuters-vectors
> #7 check the vector: reuters-vectors/tf-vectors/part-r-00000
> mahout-0.7$ hadoop fs -ls reuters-vectors/tf-vectors
> #8 Run canopy clustering to get optimal initial centroids for k-means
> mahout canopy -i
> hdfs://localhost:54310/user/bigdata/reuters-vectors/tf-vectors -o
> hdfs://localhost:54310/user/bigdata/reuters-canopy-centroids -dm
> org.apache.mahout.common.distance.CosineDistanceMeasure -t1 1500 -t2 2000
> -dm → specifying the distance measure to be used while clustering (here it is cosine distance measure)
> #9 Run k-means clustering algorithm
> mahout kmeans -i
> hdfs://localhost:54310/user/bigdata/reuters-vectors/tfidf-vectors -c
> hdfs://localhost:54310/user/bigdata/reuters-canopy-centroids -o
> hdfs://localhost:54310/user/bigdata/reuters-kmeans-clusters -cd 0.1 -ow
> -x 20 -k 10
> -i → input
> -o → output
> -c → initial centroids for k-means (not defining this parameter will
> trigger k-means to generate random initial centroids)
> -cd → convergence delta parameter
> -ow → overwrite
> -x → specifying number of k-means iterations
> -k → specifying number of clusters
> #10 Export k-means output using Cluster Dumper tool
> mahout clusterdump -dt sequencefile -d hdfs://localhost:54310/user/bigdata/reuters-vectors/dictionary.file-*
> -i hdfs://localhost:54310/user/bigdata/reuters-kmeans-clusters/clusters-8-
> final -o clusters.txt -b 15
> -dt → dictionary type
> -b → specifying length of each word
> Mahout 0.7 version did have some problems using the DisplayKmeans module which should ideally display the clusters in a 2d graph. But it gave me the same output for different input datasets. I was using dataset of recent news items that was crawled from various websites.
--
This message was sent by Atlassian JIRA
(v6.2#6252)
Re: [jira] [Closed] (MAHOUT-1450) Cleaning up clustering documentation
on mahout website
Posted by Sebastian Schelter <ss...@googlemail.com>.
No need to close stuff, we will resolve it as fixed and close it after
the next release only.
On 04/14/2014 11:15 AM, Pavan Kumar N (JIRA) wrote:
>
> [ https://issues.apache.org/jira/browse/MAHOUT-1450?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
>
> Pavan Kumar N closed MAHOUT-1450.
> ---------------------------------
>
>
>> Cleaning up clustering documentation on mahout website
>> -------------------------------------------------------
>>
>> Key: MAHOUT-1450
>> URL: https://issues.apache.org/jira/browse/MAHOUT-1450
>> Project: Mahout
>> Issue Type: Documentation
>> Components: Documentation
>> Environment: This affects all mahout versions
>> Reporter: Pavan Kumar N
>> Labels: documentation, newbie
>> Fix For: 1.0
>>
>>
>> In canopy clustering, the strategy for parallelization seems to have some dead links. Need to clean them and replace with new links (if there are any). Here is the link:
>> http://mahout.apache.org/users/clustering/canopy-clustering.html
>> Here are some details of the dead links for kmeans clustering page:
>> On the k-Means clustering - basics page,
>> first line of the Quickstart part of the documentation, the hyperlink "Here"
>> http://mahout.apache.org/users/clustering/k-means-clustering%5Equickstart-kmeans.sh.html
>> first sentence of Strategy for parallelization part of documentation, the hyperlink "Cluster computing and MapReduce", second second sentence the hyperlink "here" and last sentence the hyperlink "http://www2.chass.ncsu.edu/garson/PA765/cluster.htm" are dead.
>> http://code.google.com/edu/content/submissions/mapreduce-minilecture/listing.html
>> http://code.google.com/edu/content/submissions/mapreduce-minilecture/lec4-clustering.ppt
>> http://www2.chass.ncsu.edu/garson/PA765/cluster.htm
>> Under the page: http://mahout.apache.org/users/clustering/visualizing-sample-clusters.html
>> in the second sentence of Pre-prep part of this page, the hyperlink "setup mahout" is dead.
>> http://mahout.apache.org/users/clustering/users/basics/quickstart.html
>> The existing documentation is too ambiguous and I recommend to make the following changes so the new users can use it as tutorial.
>> The Quickstart should be replaced with the following:
>> Get the data from:
>> wget http://www.daviddlewis.com/resources/testcollections/reuters21578/reuters21578.tar.gz
>> Place it within the example folder from mahout home director:
>> mahout-0.7/examples/reuters
>> mkdir reuters
>> cd reuters
>> mkdir reuters-out
>> mv reuters21578.tar.gz reuters-out
>> cd reuters-out
>> tar -xzvf reuters21578.tar.gz
>> cd ..
>> Mahout specific Commands
>> #1 run the org.apache.lucene.benchmark .utils.ExtractReuters class
>> ${MAHOUT_HOME}/bin/mahout
>> org.apache.lucene.benchmark.utils.ExtractReuters reuters-out
>> reuters-text
>> #2 copy the file to your HDFS
>> bin/hadoop fs -copyFromLocal
>> /home/bigdata/mahout-distribution-0.7/examples/reuters-text
>> hdfs://localhost:54310/user/bigdata/
>> #3 generate sequence-file
>> mahout seqdirectory -i hdfs://localhost:54310/user/bigdata/reuters-text
>> -o hdfs://localhost:54310/user/bigdata/reuters-seqfiles -c UTF-8 -chunk 5
>> -chunk → specifying the number of data blocks
>> UTF-8 → specifying the appropriate input format
>> #4 Check the generated sequence-file
>> mahout-0.7$ ./bin/mahout seqdumper -i
>> /your-hdfs-path-to/reuters-seqfiles/chunk-0 | less
>> #5 From sequence-file generate vector file
>> mahout seq2sparse -i
>> hdfs://localhost:54310/user/bigdata/reuters-seqfiles -o
>> hdfs://localhost:54310/user/bigdata/reuters-vectors -ow
>> -ow → overwrite
>> #6 take a look at it should have 7 items by using this command
>> bin/hadoop fs -ls
>> reuters-vectors/df-count
>> reuters-vectors/dictionary.file-0
>> reuters-vectors/frequency.file-0
>> reuters-vectors/tf-vectors
>> reuters-vectors/tfidf-vectors
>> reuters-vectors/tokenized-documents
>> reuters-vectors/wordcount
>> bin/hadoop fs -ls reuters-vectors
>> #7 check the vector: reuters-vectors/tf-vectors/part-r-00000
>> mahout-0.7$ hadoop fs -ls reuters-vectors/tf-vectors
>> #8 Run canopy clustering to get optimal initial centroids for k-means
>> mahout canopy -i
>> hdfs://localhost:54310/user/bigdata/reuters-vectors/tf-vectors -o
>> hdfs://localhost:54310/user/bigdata/reuters-canopy-centroids -dm
>> org.apache.mahout.common.distance.CosineDistanceMeasure -t1 1500 -t2 2000
>> -dm → specifying the distance measure to be used while clustering (here it is cosine distance measure)
>> #9 Run k-means clustering algorithm
>> mahout kmeans -i
>> hdfs://localhost:54310/user/bigdata/reuters-vectors/tfidf-vectors -c
>> hdfs://localhost:54310/user/bigdata/reuters-canopy-centroids -o
>> hdfs://localhost:54310/user/bigdata/reuters-kmeans-clusters -cd 0.1 -ow
>> -x 20 -k 10
>> -i → input
>> -o → output
>> -c → initial centroids for k-means (not defining this parameter will
>> trigger k-means to generate random initial centroids)
>> -cd → convergence delta parameter
>> -ow → overwrite
>> -x → specifying number of k-means iterations
>> -k → specifying number of clusters
>> #10 Export k-means output using Cluster Dumper tool
>> mahout clusterdump -dt sequencefile -d hdfs://localhost:54310/user/bigdata/reuters-vectors/dictionary.file-*
>> -i hdfs://localhost:54310/user/bigdata/reuters-kmeans-clusters/clusters-8-
>> final -o clusters.txt -b 15
>> -dt → dictionary type
>> -b → specifying length of each word
>> Mahout 0.7 version did have some problems using the DisplayKmeans module which should ideally display the clusters in a 2d graph. But it gave me the same output for different input datasets. I was using dataset of recent news items that was crawled from various websites.
>
>
>
> --
> This message was sent by Atlassian JIRA
> (v6.2#6252)
>