Just the opposite. Frequent itemset would discover groups of tv channels and colors that occur together. That might be slightly interesting, but probably not so useful.
For that you want clustering, but you will have to decide how similar colors are. You might just say that if they are the same, distance is 0 while different means distance 1. Or you could do SVD first and then cluster (that is spectral clustering, ish). 2011/8/18 Clément Notin <[email protected]> > 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 <[email protected]> > > > 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 <[email protected]> > > > > > 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* >
