Pavan Kumar N created MAHOUT-1450:
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Summary: Cleaning up k-means 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
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.
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