Mahout

TL;DR

A project from the Apache Software Foundation that provides scalable machine learning algorithms and libraries.
Built to handle large-scale, big-data processing (can work with data sets too large for a single machine).
Includes algorithms for classification, regression, clustering, and dimensionality reduction.

Definition

Mahout is a project of the Apache Software Foundation that aims to provide scalable machine learning algorithms and libraries. It is particularly well-suited for large-scale data processing tasks, such as those commonly encountered in big data environments.

Explanation

Mahout is designed for large-scale machine learning by providing a variety of algorithms and libraries that scale to large data sets. Because Mahout is built on top of Apache Hadoop, it can handle data that do not fit on a single machine, making it suitable for common big data tasks. The project includes algorithms across multiple machine learning categories — classification, regression, clustering, and dimensionality reduction — allowing it to be applied in diverse settings.

Examples

Collaborative filtering / Recommendation engine

A company might use Mahout to build a recommendation engine for its online shopping site. By analyzing the items that users have purchased in the past, Mahout can predict which items are likely to be of interest to each user and make personalized recommendations accordingly.

Clustering / Customer segmentation

A marketing company might use Mahout to cluster its customers into different segments based on their demographics and purchasing behavior. This can help the company target its marketing efforts more effectively by tailoring its messaging to each segment.

Use cases

Online retail
Marketing
Finance

Apache Hadoop
Collaborative filtering
Clustering
Classification
Regression
Dimensionality reduction