Introduction of Apache Mahout , Algorithm of Mahout, comparison between Mahout and R

1. By : Puneet Gupta
M.Tech (Future Studies and Planning)

2.  A mahout is one who drives an elephant as its
master
 Its close association with Apache Hadoop
which uses an elephant as its logo.
 Apache Mahout started as a sub-project of
Apache’s Lucene in 2008. In 2010, Mahout
became a top level project of Apache.

3. Apache Mahout?
• Apache Mahout is an open source project
• Mahout is a Java library
- Implementing Machine Learning techniques
• Recommendation
• Clustering
• Classification

4. What can we do?
•Currently Mahout supports mainly three use cases:
–Recommendation - takes users' behavior and from
that tries to find items users might like.
–Clustering - takes e.g. text documents and groups
them into groups of topically related documents.
–Classification - learns from existing categorized
documents what documents of a specific category
look like and is able to assign unlabeled documents
to the (hopefully) correct category.

5. Why Mahout?
• Mahout is not the only Machine Learning
framework
– Weka
– R
• Why do we prefer Mahout?
– Apache License
– Good Community
– Good Documentation
– Scalable
•Based on Hadoop (not mandatory!)

6. Why do need a scalable framework?

7. Algorithms
•Recommendation
– User-based Collaborative Filtering
– Item-based Collaborative Filtering
– Slope One Recommenders
– Singular Value Decomposition

8. Algorithms
•Clustering
- Canopy
- K-Means
- Fuzzy K-Means
- Latent Dirichlet Allocation (LDA)
- MinHash Clustering
- Hierarchical Clustering

9. Algorithms
•Classification
- Logistic Regression
- Bayes
- Random Forests
- Hidden Markov Models
- Support Vector Machines
- Neural Networks
- Restricted Boltzmann Machines

10. Mahout Vs R
• Mahout is a java library But R is an
expression language with a very simple
syntax.
• Mahout use for Big data and R use
prototype data