Command Line commands for Kmeans Clustering
./bin/mahout seqdirectory -i ./examples/bin/work/reuters-out/ -o ./examples/bin/work/reuters-out-seqdir -c UTF-8 -chunk 5
./bin/mahout seq2sparse -i ./examples/bin/work/reuters-out-seqdir/ -o ./examples/bin/work/reuters-out-seqdir-sparse -ng 2 -nv
./bin/mahout kmeans -i ./examples/bin/work/reuters-out-seqdir-sparse/tfidf-vectors/ -c ./examples/bin/work/clusters -o ./examples/bin/work/reuters-kmeans -x 10 -k 2 -ow -cl
./bin/mahout clusterdump -d ./examples/bin/work/reuters-out-seqdir-sparse/dictionary.file-0 -dt sequencefile -s ./examples/bin/work/reuters-kmeans/clusters-3/part-r-00000 -n 20 -b 100 -p ./examples/bin/work/reuters-kmeans/clusteredPoints
./bin/mahout seqdumper -s ./examples/bin/work/reuters-kmeans/clusteredPoints/part-m-00000 | more
./bin/mahout rowid -i ./examples/bin/work/reuters-out-seqdir-sparse/tfidf-vectors/part-r-00000 -o ./examples/bin/work/reuters-matrix
./bin/mahout rowid -Dmapred.input.dir=/home/venkat/Desktop/mahout/mahout-distribution-0.5/examples/bin/work/reuters-out-seqdir-sparse/tfidf-vectors/part-r-00000 -Dmapred.output.dir=/home/venkat/Desktop/mahout/mahout-distribution-0.5/examples/bin/work/reuters-matrix
4 rows and 1073 columns
./bin/mahout seqdumper -s /home/venkat/Desktop/mahout/mahout-distribution-0.5/examples/bin/work/reuters-matrix/matrix | more
./bin/mahout rowsimilarity -i /home/venkat/Desktop/mahout/mahout-distribution-0.5/examples/bin/work/reuters-matrix/matrix
-o /home/venkat/Desktop/mahout/mahout-distribution-0.5/examples/bin/work/reuters-named-similarity
-r 1073
--similarityClassname SIMILARITY_COSINE -m 10
./bin/mahout rowsimilarity -i /home/venkat/Desktop/mahout/mahout-distribution-0.5/examples/bin/work/reuters-matrix/matrix -o /home/venkat/Desktop/mahout/mahout-distribution-0.5/examples/bin/work/reuters-named-similarity -r 1073 --similarityClassname SIMILARITY_COOCCURRENCE -m 10 --tempDir /home/venkat/Desktop/tmp
SIMILARITY_COOCCURRENCE,
SIMILARITY_EUCLIDEAN_DISTANCE,
SIMILARITY_LOGLIKELIHOOD,
SIMILARITY_PEARSON_CORRELATION,
SIMILARITY_TANIMOTO_COEFFICIENT,
SIMILARITY_UNCENTERED_COSINE,
SIMILARITY_UNCENTERED_ZERO_ASSUMING_COSINE,
SIMILARITY_CITY_BLOCK
./bin/mahout seqdumper -s /home/venkat/Desktop/mahout/mahout-distribution-0.5/examples/bin/work/reuters-matrix/docIndex
Canopy
./bin/mahout canopy -i ./examples/bin/work/reuters-out-seqdir-sparse/tfidf-vectors/ -o ./examples/bin/work/canopy-output -t1 3.0 -t2 2.8 -t3 3.0 -t4 2.8 -ow -xm sequential
./bin/mahout kmeans -i ./examples/bin/work/reuters-out-seqdir-sparse/tfidf-vectors/ -c ./examples/bin/work/canopy-output -o ./examples/bin/work/reuters-kmeans -x 10 -k 3 -ow
./bin/mahout kmeans -i ./examples/bin/work/reuters-out-seqdir-sparse/tfidf-vectors/ -c ./examples/bin/work/canopy-output -o ./examples/bin/work/reuters-kmeans -x 10 -ow
./bin/mahout seqdumper -s ./examples/bin/work/reuters-kmeans/clusters-1/part-r-00000
bin/mahout canopy
-i ./examples/bin/work/reuters-out-seqdir-sparse/tfidf-vectors/
-o ./examples/bin/work/reuters-out-seqdir-sparse
-dm new ManhattanDistanceMeasure()
-t1 3.0
-t2 2.8
-t3 3.0
-t4 2.8
-ow
-cl <run input vector clustering after computing Canopies>
-xm sequential
Usage:
[--minSupport <minSupport> --analyzerName <analyzerName> --chunkSize
<chunkSize> --output <output> --input <input> --minDF <minDF> --maxDFPercent
<maxDFPercent> --weight <weight> --norm <norm> --minLLR <minLLR> --numReducers
<numReducers> --maxNGramSize <ngramSize> --overwrite --help
--sequentialAccessVector --namedVector --logNormalize]
Options
--minSupport (-s) minSupport (Optional) Minimum Support. Default
Value: 2
--analyzerName (-a) analyzerName The class name of the analyzer
--chunkSize (-chunk) chunkSize The chunkSize in MegaBytes. 100-10000 MB
--output (-o) output The output directory
--input (-i) input input dir containing the documents in
sequence file format
--minDF (-md) minDF The minimum document frequency. Default
is 1
--maxDFPercent (-x) maxDFPercent The max percentage of docs for the DF.
Can be used to remove really high
frequency terms. Expressed as an integer
between 0 and 100. Default is 99.
--weight (-wt) weight The kind of weight to use. Currently TF
or TFIDF
--norm (-n) norm The norm to use, expressed as either a
float or "INF" if you want to use the
Infinite norm. Must be greater or equal
to 0. The default is not to normalize
--minLLR (-ml) minLLR (Optional)The minimum Log Likelihood
Ratio(Float) Default is 1.0
--numReducers (-nr) numReducers (Optional) Number of reduce tasks.
Default Value: 1
--maxNGramSize (-ng) ngramSize (Optional) The maximum size of ngrams to
create (2 = bigrams, 3 = trigrams, etc)
Default Value:1
--overwrite (-ow) If set, overwrite the output directory
--help (-h) Print out help
--sequentialAccessVector (-seq) (Optional) Whether output vectors should
be SequentialAccessVectors. If set true
else false
--namedVector (-nv) (Optional) Whether output vectors should
be NamedVectors. If set true else false
--logNormalize (-lnorm) (Optional) Whether output vectors should
be logNormalize. If set true else false
