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*
