Q21Big Data Analytics

Mahout provides implementations of various machine learning algorithms focused primarily on collaborative filtering, clustering, and classification. 1. Collaborative Filtering: Algorithms to build recommendation engines (e.g., user-based or item-based recommenders). 2. Clustering: Grouping items based on similarity without prior labels. Mahout includes implementations of K-Means, Fuzzy K-Means, and Canopy clustering. 3. Classification: Categorizing unseen data into pre-defined classes. Mahout supports algorithms like Naive Bayes, Random Forests, and Hidden Markov Models. Initially, Mahout used MapReduce for distributed processing, but it has largely migrated to Apache Spark, Flink, and H2O to take advantage of their much faster in-memory processing capabilities via the Samsara vector math experimentation environment.