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From Dave Fry <d...@upstreamsoftware.com>
Subject Frequent itemset mining
Date Fri, 02 Dec 2011 02:51:09 GMT
Hi!  I apologize for the newbie question, I'm just getting started with
Mahout.

On the "Overview" page on Mahout's website:
https://cwiki.apache.org/confluence/display/MAHOUT/Overview

It mentions this as the four primary targeted use cases for Mahout:
1) Recommendation mining takes users' behavior and from that tries to find
items users might like.
2) Clustering takes e.g. text documents and groups them into groups of
topically related documents.
3) Classification learns from exisiting categorized documents what
documents of a specific category look like and is able to assign unlabelled
documents to the (hopefully) correct category.
4) Frequent itemset mining takes a set of item groups (terms in a query
session, shopping cart content) and identifies, which individual items
usually appear together.

But, based on the Mahout documentation that I've read through, I can't seem
to find a clear mapping from that use case description to where in the
Mahout distribution I should be looking.  I've found several leads for use
case #1, but #4 seems to be a bit of a mystery (and searches for "frequent
itemset mining" don't seem to lead me to where I need to go.)

Basically, I'm looking to the answer to the question "Which items appear
most often with item X in browse histories and shopping carts?".  (As
opposed to "Based on what I know about your preferences, here are the items
that I predict you would be most likely to browse/add to your cart".)

Any help is appreciated!
Thanks,
Dave

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