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From Clément Notin <clement.no...@gmail.com>
Subject Re: Difference between clustering and frequent itemset mining
Date Thu, 18 Aug 2011 07:53:14 GMT
Ok thanks !

So if I want to discover groups of customers based on, for example, their
favorite color, their favorite TV channel and the brand of their cellular
phone (it's an example...) should I use frequent itemset mining instead of
clustering ?

2011/8/17 Ted Dunning <ted.dunning@gmail.com>

> Both clustering and frequent itemset algorithms are unsupervised learning
> methods.
>
> Clustering uses your definition of near and far to find (hopefully) clumps
> of data.
>
> Frequent item-set analysis looks for cases where items cooccur.  The origin
> is in what is called market-basket analysis where the goal was to find
> items
> that are commonly purchased together.
>
> For most purposes, I recommend simple cooccurrence analysis.
>
> I think that your confusion stems from you telling the frequent itemset
> code
> to find item characteristics that often occur together on the same item.
>  That probably isn't what you want.
>
> 2011/8/17 Clément Notin <clement.notin@gmail.com>
>
> > Hello Mahout !
> >
> > I'm unable to find the answer (trust me, I tried !) of a simple question
> :
> > what is the difference between clustering and frequent itemset mining ?
> >
> > I think that frequent itemset mining could help me to cluster things
> based
> > on colors or other non-numerical characteristics. I thought about
> > converting
> > these values to numbers but it don't always make sense (what order should
> I
> > use ? blue is near purple ok so blue = 1 and purple = 2 but is these car,
> > for example, near that one ?).
> >
> > Thanks for reading.
> > Regards,
> >
> > --
> > *Clément **Notin*
> >
>



-- 
*Clément **Notin*

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