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From Sebastian Schelter <>
Subject Re: Mahout fpg
Date Wed, 20 Nov 2013 07:22:07 GMT
You can use ItemSimilarityJob to find sets of items that cooccur
together in your users interactions.


On 20.11.2013 08:11, Sameer Tilak wrote:
> Hi Sunil,
> Thanks for your reply. We can benefit a lot from the parallel frequent pattern matching
functionality. Will there be any alternative in future releases? I guess, we can use older
versions of Mahout if we need that.
>> Date: Tue, 19 Nov 2013 19:25:54 -0800
>> From:
>> Subject: Re: Mahout fpg
>> To:
>> Fpg has been removed from the codebase as it will not be supported.
>> On Tuesday, November 19, 2013 8:56 PM, Sameer Tilak <> wrote:
>> Hi everyone,I downloaded the latest version of Mahout and did mvn install. When I
try to run fog, I get the following errors. Do I need to download and compile FPG separately?
Looks like somehow it has not been included in the list of valid programs.
>> 13/11/19 17:49:19 WARN driver.MahoutDriver: Unable to add class: fpg13/11/19 17:49:19
WARN driver.MahoutDriver: No fpg.props found on classpath, will use command-line arguments
onlyUnknown program 'fpg' chosen.Valid program names are:  arff.vector: : Generate Vectors
from an ARFF file or directory  baumwelch: : Baum-Welch algorithm for unsupervised HMM training
 canopy: : Canopy clustering  cat: : Print a file or resource as the logistic regression models
would see it  cleansvd: : Cleanup and verification of SVD output  clusterdump: : Dump cluster
output to text  clusterpp: : Groups Clustering Output In Clusters  cmdump: : Dump confusion
matrix in HTML or text formats  concatmatrices: : Concatenates 2 matrices of same cardinality
into a single matrix  cvb: : LDA via Collapsed Variation Bayes (0th deriv. approx)  cvb0_local:
: LDA via Collapsed Variation Bayes, in memory locally.  evaluateFactorization: : compute
RMSE and MAE of a rating
>>  matrix factorization against probes  fkmeans: : Fuzzy K-means clustering  hmmpredict:
: Generate random sequence of observations by given HMM  itemsimilarity: : Compute the item-item-similarities
for item-based collaborative filtering  kmeans: : K-means clustering  lucene.vector: : Generate
Vectors from a Lucene index  lucene2seq: : Generate Text SequenceFiles from a Lucene index
 matrixdump: : Dump matrix in CSV format  matrixmult: : Take the product of two matrices 
parallelALS: : ALS-WR factorization of a rating matrix  qualcluster: : Runs clustering experiments
and summarizes results in a CSV  recommendfactorized: : Compute recommendations using the
factorization of a rating matrix  recommenditembased: : Compute recommendations using item-based
collaborative filtering  regexconverter: : Convert text files on a per line basis based on
regular expressions  resplit: : Splits a set of SequenceFiles into a number of equal splits
 rowid: :
>>  Map SequenceFile<Text,VectorWritable> to {SequenceFile<IntWritable,VectorWritable>,
SequenceFile<IntWritable,Text>}  rowsimilarity: : Compute the pairwise similarities
of the rows of a matrix  runAdaptiveLogistic: : Score new production data using a probably
trained and validated AdaptivelogisticRegression model  runlogistic: : Run a logistic regression
model against CSV data  seq2encoded: : Encoded Sparse Vector generation from Text sequence
files  seq2sparse: : Sparse Vector generation from Text sequence files  seqdirectory: : Generate
sequence files (of Text) from a directory  seqdumper: : Generic Sequence File dumper  seqmailarchives:
: Creates SequenceFile from a directory containing gzipped mail archives  seqwiki: : Wikipedia
xml dump to sequence file  spectralkmeans: : Spectral k-means clustering  split: : Split Input
data into test and train sets  splitDataset: : split a rating dataset into training and probe
parts  ssvd: :
>>  Stochastic SVD  streamingkmeans: : Streaming k-means clustering  svd: : Lanczos
Singular Value Decomposition  testnb: : Test the Vector-based Bayes classifier  trainAdaptiveLogistic:
: Train an AdaptivelogisticRegression model  trainlogistic: : Train a logistic regression
using stochastic gradient descent  trainnb: : Train the Vector-based Bayes classifier  transpose:
: Take the transpose of a matrix  validateAdaptiveLogistic: : Validate an AdaptivelogisticRegression
model against hold-out data set  vecdist: : Compute the distances between a set of Vectors
(or Cluster or Canopy, they must fit in memory) and a list of Vectors  vectordump: : Dump
vectors from a sequence file to text  viterbi: : Viterbi decoding of hidden states from given
output states sequence                           

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