Apache Mahout is a machine-learning tool. By enabling you to build machine-learning libraries that are scalable to "reasonably large" datasets, it aims to make building intelligent applications easier and faster.
The main use cases for Mahout are:
- Recommendation mining, which tries to identify things users will like on the basis of their past behavior (for example shopping or online-content recommendations)
- Clustering, which groups similar items (for example, documents on similar topics)
- Classification, which learns from existing categories what members of each category have in common, and on that basis tries to categorize new items
- Frequent item-set mining, which takes a set of item-groups (such as terms in a query session, or shopping-cart content) and identifies items that usually appear together