You can use ItemSimilarityJob to find sets of items that cooccur
together in your users interactions.
sebastian
On 20.11.2013 08:11, Sameer Tilak wrote:
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> 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.
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>> Date: Tue, 19 Nov 2013 19:25:54 0800
>> From: suneel_marthi@yahoo.com
>> Subject: Re: Mahout fpg
>> To: user@mahout.apache.org
>>
>> Fpg has been removed from the codebase as it will not be supported.
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>> On Tuesday, November 19, 2013 8:56 PM, Sameer Tilak <sstilak@live.com> wrote:
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>> 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 commandline arguments
onlyUnknown program 'fpg' chosen.Valid program names are: arff.vector: : Generate Vectors
from an ARFF file or directory baumwelch: : BaumWelch 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 Kmeans clustering hmmpredict:
: Generate random sequence of observations by given HMM itemsimilarity: : Compute the itemitemsimilarities
for itembased collaborative filtering kmeans: : Kmeans 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: : ALSWR 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 itembased
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 kmeans 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 kmeans clustering svd: : Lanczos
Singular Value Decomposition testnb: : Test the Vectorbased Bayes classifier trainAdaptiveLogistic:
: Train an AdaptivelogisticRegression model trainlogistic: : Train a logistic regression
using stochastic gradient descent trainnb: : Train the Vectorbased Bayes classifier transpose:
: Take the transpose of a matrix validateAdaptiveLogistic: : Validate an AdaptivelogisticRegression
model against holdout 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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