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https://issues.apache.org/jira/browse/MAHOUT-588?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12989223#comment-12989223
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Szymon Chojnacki commented on MAHOUT-588:
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Collocations bottle-neck.
As I see, so far both of us did vectorization with 1-grams. I must admit that I
am affraid to calculate 2-grams or 3-grams. My understanding of:
DictionaryVectorizer
CollocDriver
CollocMapper
CollocReducer
is that, firstly all collocations are built and counted and only those with
freq>minCount are restored. If it is so, I think that one more preprocessing
step would make it faster in our setting (huge amount of typos and a
long,long-tail) - i.e. building collocations only for tokens (1-grams) with
freq>minCount. I'll experiment with this issue few hours today.
> Benchmark Mahout's clustering performance on EC2 and publish the results
> ------------------------------------------------------------------------
>
> Key: MAHOUT-588
> URL: https://issues.apache.org/jira/browse/MAHOUT-588
> Project: Mahout
> Issue Type: Task
> Reporter: Grant Ingersoll
> Attachments: SequenceFilesFromMailArchives.java,
> SequenceFilesFromMailArchives2.java, Top1000Tokens_maybe_stopWords,
> Uncompress.java, clusters_kMeans.txt, distcp_large_to_s3_failed.log,
> seq2sparse_small_failed.log, seq2sparse_xlarge_ok.log
>
>
> For Taming Text, I've commissioned some benchmarking work on Mahout's
> clustering algorithms. I've asked the two doing the project to do all the
> work in the open here. The goal is to use a publicly reusable dataset (for
> now, the ASF mail archives, assuming it is big enough) and run on EC2 and
> make all resources available so others can reproduce/improve.
> I'd like to add the setup code to utils (although it could possibly be done
> as a Vectorizer) and the publication of the results will be put up on the
> Wiki as well as in the book. This issue is to track the patches, etc.
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