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https://issues.apache.org/jira/browse/MAHOUT-157?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12744161#action_12744161
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Robin Anil commented on MAHOUT-157:
-----------------------------------
Added javadocs.
PFPgrowth returns correct solution for every data i have put through it. I
still feel the Vanilla FPGrowth and Top K FPGrowth implementation is slow for
very small minSupport values.
Also it occurred to me FPGrowth could be replaced easily by any frequent
itemset Mining Algorithm like Apriori and the rest of the code(Partitioning and
Aggregating) could remain exactly the same. So I propose to go the Algorithm
Interface way like I did in Bayes/CBayes. I am also waiting for MAHOUT-108 to
see if its doing something drastically different from data partitioning
employed here.
> Frequent Pattern Mining using Parallel FP-Growth
> ------------------------------------------------
>
> Key: MAHOUT-157
> URL: https://issues.apache.org/jira/browse/MAHOUT-157
> Project: Mahout
> Issue Type: New Feature
> Affects Versions: 0.2
> Reporter: Robin Anil
> Fix For: 0.2
>
> Attachments: MAHOUT-157-August-17.patch, MAHOUT-157-August-6.patch,
> MAHOUT-157-Combinations-BSD-License.patch,
> MAHOUT-157-Combinations-BSD-License.patch,
> MAHOUT-157-inProgress-August-5.patch
>
>
> Implement: http://infolab.stanford.edu/~echang/recsys08-69.pdf
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