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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>
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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.
>
>> Date: Tue, 19 Nov 2013 19:25:54 -0800
>> From: suneel_mar...@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 <ssti...@live.com> 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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