Apache Mahout is an open source machine learning library that provides scalable machine learning algorithms focused on clustering, classification, and collaborative filtering. It allows building scalable machine learning tools for analyzing big data in a distributed manner using frameworks like Hadoop. Some key algorithms supported include logistic regression, Bayesian classification, k-means clustering, and item-based collaborative filtering. Companies are using Mahout for applications like movie recommendations, fraud detection, and ad recommendations by taking advantage of its scalability for large datasets.

1. Apache
The Elephant Driver
Presenters:
Antonio Loureiro Severien
Emmanouil Dimogerontakis
Muhammad Anis uddin Nasir

2. What is Apache Mahout?
● Machine learning and data mining framework for
classification, clustering and recommendation
● The Apache Mahout free machine learning library's goal
is to build scalable machine learning tools for use on
analysing big data on a distributed manner

3. Machine Learning
"Machine Learning is programming computers to optimize a
performance criterion using example data or past
experience" - Alpaydin, 2004
Machine learning is concerned with the design and
development of algorithms that allow machines to make
decisions or even evolve behaviors based on collection of
empirical data.

4. Data Mining
Data mining, also called knowledge discovery in
databases(KDD) is the process of discovering interesting
and useful patterns and relationships in large volumes of
data.
Combines tools from:
● statistics
● artificial intelligence (such as neural networks and
machine learning)
with database management to analyze large data sets.
-Britannica Online Encyclopedia

5. Why Machine Learning and Data
Mining?
● Data, Data, DATA!!!
● Tasks too Hard to Program
● Customizing software

6. Available Machine Learning Tools
● WEKA
● R
● KEEL
● Others...
Not enough?

7. Apache Mahout vs others?
Many open source Machine Learning
libraries either:
● Lack Community
● Lack Documentation and Examples
● Lack the Apache License
(business opportunity)
● Are research-oriented
(not fit for production yet)
● Lack Scalability

8. Mahout = Elephant Driver?

9. Why we need scalability?
● Big Data

10. Applications
● Recommendation features
● Clustering of information
● Classification
Examples: Movie recommendations, stock
analysis, fraud detection, ad-sense
recommendation, etc...
How do we do this?

11. Supported Algorithms
● Classification
● Clustering
● Recommender / Collaborative Filtering
● Evolutionary Algorithms
● Pattern Mining
● Regression
● Dimension reduction
● Similarity Vectors

12. Classification
(learn to assign categories to documents)
Fully functional
● Logistic Regression (SGD)
● Bayesian
Integrated to Mahout Development
● Random Forests (integrated)
● Online Passive Aggressive (integrated)
● Boosting (awaiting patch commit)
Open to be worked on...
● Hidden Markov Models (HMM) - Training is done in Map-Reduce
● Support Vector Machines (SVM) (open)
● Perceptron and Winnow (open)
● Neural Network (open)

13. Clustering
(group items that are topically related)
Fully functional
● Expectation Maximization (EM)
● Hierarchical Clustering
Integrated to Mahout Development
● Canopy Clustering
● K-Means Clustering
● Fuzzy K-Means
● Mean Shift Clustering
● Dirichlet Process Clustering
● Latent Dirichlet Allocation
● Spectral Clustering
● Minhash Clustering
● Top Down Clustering

14. Recommenders /
Collaborative Filtering
(find items a user might like /
find items that appear together)
Integrated to Mahout Development
● Non-distributed recommenders ("Taste") (integrated)
● Distributed Item-Based Collaborative Filtering (integrated)
● Collaborative Filtering using a parallel matrix factorization (integrated)

15. Who is using it?

16. Opportunities
● Developers
● Researchers
● Small Business
● Large Business
● Consultancy...
○ on Mahout
○ on specific data analysis
● Open data
● etc...

17. Apache Mahout
Business?
Ideas?
Suggestions?
Questions?

18. Where to start?
● Wikipedia Bayes Example
○ https://cwiki.apache.org/MAHOUT/wikipedia-bayes-example.html
● What does it do?
○ Classify wikipedia data dump by countries.
○ Objective: Predict what country an unseen article
should be categorized into.

19. References
General
http://www.slideshare.net/sdec2011/sdec2011-mahout-the-what-the-how-and-
the-why
http://www.slideshare.net/gsingers/intro-to-mahout-dc-hadoop
http://www.slideshare.net/aneeshabakharia/lca2011-mahout
Hands-on
http://www.slideshare.net/OReillyOSCON/hands-on-mahout
Who is using it?
https://cwiki.apache.org/MAHOUT/powered-by-mahout.html
Apache Mahout
http://mahout.apache.org/
Quickstart
https://cwiki.apache.org/MAHOUT/quickstart.html