Hi Siddharth,
Your question is not very clear on what (either
keywords/documents) you want to cluster.
BTW if you are looking for document clustering its straight
approach using the term/keyword weights and you can find documentation in
Mahout in Action or some other
Great suggestion!
Will do.
On Fri, Jan 25, 2013 at 1:10 AM, Sean Owen sro...@gmail.com wrote:
Why not test both the original and pruned data set? The low-rating
data may still help, even when the rating is forgotten.
I would not base the decision just on whether you can make
recommendations
I have successfully run the Breiman example from
https://cwiki.apache.org/confluence/display/MAHOUT/Breiman+Example
How do I view the tree? Do I need to write a program that instantiates
ForestVisualizer.java? Is there another program for visualizing the results
of Mahout output?
Many
The way I do it is to set x different for each user, to the number of
items in the user's test set -- you ask for x recommendations.
This makes precision == recall, note. It dodges this problem though.
Otherwise, if you fix x, the condition you need is stronger, really:
each user needs = x *test
Interesting. Using
IRStatistics stats = evaluator.evaluate(recommenderBuilder,
null, model, null, 5,
GenericRecommenderIRStatsEvaluator.CHOOSE_THRESHOLD,
1.0);
Can it be adjusted to each user ? In other
No, it takes a fixed at value. You can modify it to do whatever you want.
You will see it doesn't bother with users with little data, like
2*at data points.
On Fri, Jan 25, 2013 at 6:23 PM, Zia mel ziad.kame...@gmail.com wrote:
Interesting. Using
IRStatistics stats =
