Mahout

Mahout, short for machine learning, refers to Apache Mahout, an open-source project of the Apache Software Foundation that provides scalable machine-learning algorithms. Built to enable data scientists to work with very large data sets, Mahout concentrates on producing core recommender systems, classification, clustering, and dimensionality reduction algorithms that can run on distributed computing platforms.

Historically, Mahout started as a collection of MapReduce implementations for Hadoop, designed to scale with the underlying storage and processing engine. Over time, the project broadened to support alternative backends and a mathematical engine known as Samsara, which provides dense and sparse linear algebra primitives used by many Mahout algorithms. This architecture allows the same high-level algorithms to run on different processing engines, such as Hadoop MapReduce and Apache Spark, and to operate on large sparse data common in recommender systems and text classification.

Algorithms available in Mahout cover clustering, classification, collaborative filtering, and dimensionality reduction, including k-means, canopy, logistic regression, Naive Bayes, matrix factorization for recommender systems, and singular value decomposition. The library emphasizes scalability, interoperability with the Hadoop ecosystem, and a stable API for building scalable analytics pipelines. Mahout is distributed under the Apache License 2.0, remains an active Apache project, and is used in research and production settings to prototype and deploy machine-learning solutions on big data.