- General
- Developers
- Mahout-Samsara
- Algorithms
- List of algorithms
- Distributed Matrix Decomposition
- Cholesky QR
- SSVD
- Distributed ALS
- SPCA
- Recommendations
- Recommender Overview
- Intro to cooccurrence-based
recommendations with Spark - Classification
- Spark Naive Bayes
- MapReduce Basics
- List of algorithms
- Overview
- Working with text
- Creating vectors from text
- Collocations
- Dimensionality reduction
- Singular Value Decomposition
- Stochastic SVD
- Topic Models
- Latent Dirichlet Allocation
- Mahout MapReduce
- Classification
- Naive Bayes
- Hidden Markov Models
- Logistic Regression (Single Machine)
- Random Forest
- Classification Examples
- Breiman example
- 20 newsgroups example
- SGD classifier bank marketing
- Wikipedia XML parser and classifier
- Clustering
- k-Means
- Canopy
- Fuzzy k-Means
- Streaming KMeans
- Spectral Clustering
- Clustering Commandline usage
- Options for k-Means
- Options for Canopy
- Options for Fuzzy k-Means
- Clustering Examples
- Synthetic data
- Cluster Post processing
- Cluster Dumper tool
- Cluster visualisation
- Recommendations
- First Timer FAQ
- A user-based recommender
in 5 minutes - Matrix factorization-based
recommenders - Overview
- Intro to item-based recommendations
with Hadoop - Intro to ALS recommendations
with Hadoop
Running Canopy Clustering from the Command Line
Mahout’s Canopy clustering can be launched from the same command line invocation whether you are running on a single machine in stand-alone mode or on a larger Hadoop cluster. The difference is determined by the $HADOOP_HOME and $HADOOP_CONF_DIR environment variables. If both are set to an operating Hadoop cluster on the target machine then the invocation will run Canopy on that cluster. If either of the environment variables are missing then the stand-alone Hadoop configuration will be invoked instead.
./bin/mahout canopy <OPTIONS>
- In $MAHOUT_HOME/, build the jar containing the job (mvn install) The job will be generated in $MAHOUT_HOME/core/target/ and it’s name will contain the Mahout version number. For example, when using Mahout 0.3 release, the job will be mahout-core-0.3.job
Testing it on one single machine w/o cluster
- Put the data: cp
testdata -
Run the Job:
./bin/mahout canopy -i testdata -o output -dm org.apache.mahout.common.distance.CosineDistanceMeasure -ow -t1 5 -t2 2
Running it on the cluster
- (As needed) Start up Hadoop: $HADOOP_HOME/bin/start-all.sh
- Put the data: $HADOOP_HOME/bin/hadoop fs -put
testdata -
Run the Job:
export HADOOP_HOME=
export HADOOP_CONF_DIR=$HADOOP_HOME/conf ./bin/mahout canopy -i testdata -o output -dm org.apache.mahout.common.distance.CosineDistanceMeasure -ow -t1 5 -t2 2 - Get the data out of HDFS and have a look. Use bin/hadoop fs -lsr output to view all outputs.
Command line options
--input (-i) input Path to job input directory.Must
be a SequenceFile of
VectorWritable
--output (-o) output The directory pathname for output.
--overwrite (-ow) If present, overwrite the output
directory before running job
--distanceMeasure (-dm) distanceMeasure The classname of the
DistanceMeasure. Default is
SquaredEuclidean
--t1 (-t1) t1 T1 threshold value
--t2 (-t2) t2 T2 threshold value
--clustering (-cl) If present, run clustering after
the iterations have taken place
--help (-h) Print out help