They are both for machine learning. Classification is known as "supervised learning" where you feed the engine data of known patterns and instruct it what are the key nodes. Clustering is "unsupervised learning" where you allow the algorithm to "guess" at what is significant in the correlations picked up by the algorithm. Spam filtering is a popular example of classification, and image indexing is a popular example of clustering. It is mainly used on Hadoop because when it comes to machine learning, the more data that passes through the algorithm the more accurate it should be, and Hadoop can handle large data better than anything else around at the moment.
*Devin Suiter* Jr. Data Solutions Software Engineer 100 Sandusky Street | 2nd Floor | Pittsburgh, PA 15212 Google Voice: 412-256-8556 | www.rdx.com On Fri, Nov 22, 2013 at 2:54 AM, unmesha sreeveni <[email protected]>wrote: > what is the differences b/w classification algorithms and clustering > algorithms in hadoop? > > > -- > *Thanks & Regards* > > Unmesha Sreeveni U.B > > *Junior Developer* > > >
