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* >
