The document discusses Apache Mahout, an open-source machine learning library in Java, covering its main techniques and architecture for various applications like search text mining and collaborative filtering. It details user and item preference modeling, classification systems using methods like Naïve Bayes, and clustering methods such as k-means. The document concludes with insights into the stability of its components and opens the floor for questions.

1. MAHOUT
Dalla Palma Stefano
Open source Machine Learning Java library
A P A C H E
Part I: Overview

2. Search
Text mining
Information Retrieval

3. Search
Text mining
Information Retrieval

4. Search
Text mining
Information Retrieval
Collaborative
filtering

5. Search
Text mining
Information Retrieval
Collaborative
filtering

6. Mahout Machine Learning main techniques
and
architecture overview

7. Recommender
Data Store
User
Preference Item
Preference Item
Recommender
Neighborhood
Correlation
Preference
InferrerData Model
User
User

8. Classification
(Naïve Bayes)
Training
examples
Training
algorithm
ModelNew examples Decisions
Predictors and
target variables
Classification System
Copy
Estimated
target
variable
Predicted
variables
only
Model

9. Clustering

10. Clustering

11. Clustering
(k-means)
Map output = <centroid_id, data_point>
S = Shuffle and Sorting
M1
Split 1
Split 2
Split n-1
Split n
…Input M2
Mn
R1
R2
Rk
S
New Centroids
file on Distr.
Cache
Centroids file on
Distr. Cache
Map phase Reduce phase

13. Samsara
Apache Flink

14. Flink

15. MAHOUTOpen source Machine Learning Java library
A P A C H E
Part II: Architecture

38. MAHOUTOpen source Machine Learning Java library
A P A C H E
Part III: Conclusions

44. Stablest components

45. Most instable components

51. Questions?
Dalla Palma Stefano
A P A C H E M A H O U T