Basic of Mahout

 
Mahout is basically Machine Learning algorithms which solve 3 major problems.

1. Recommendation.
   
2. Clustering
   
3. Classification
   

Recommendation :
Recommendation will recommend a similar taste of items where the user is really interested in . Basically Recommendation is done by based on the user activity based on history. In general there are 3 types of recommendation

• User Based
  
• Item Based
  
• Content Based
  

User Based Recommendation:
Lets take real time example as amazon book purchase. when a user purchase any books in amazon, Amazon guys are recommending some more items along with that which are similar to the user taste


Item Based Recommendation:
Real time Example is Facebook recommends a friends for you. If you noticed the friends which they are recommending with be some what known the user.


Clustering :

Clustering is a process of grouping the text documents into groups of topically related documents.Clustering done based on TF-IDF

• K-Means
• Mean Shifting
• Fuzzy K-Means

Classification :

Classification learns from exisiting categorized documents what documents of a specific category look like and is able to assign unlabelled documents to the (hopefully) correct category.

Above all the Methods are readily available . But our main work is to preparing the dataset in a proper way in which it can produce the efficient result.