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Apache Mahout Tutorials

Welcome to Apache Mahout Tutorials. The objective of these tutorials is to provide in depth understand of Apache Mahout.

In addition to free Apache Mahout Tutorials, we will cover common interview questions, issues and how to’s of Apache Mahout.


A mahout is one who drives an elephant as its master. The name comes from its close association with Apache Hadoop which uses an elephant as its logo.

Hadoop is an open-source framework from Apache that allows to store and process big data in a distributed environment across clusters of computers using simple programming models.

Apache Mahout is an open source project that is primarily used for creating scalable machine learning algorithms. It implements popular machine learning techniques such as:




Apache Mahout started as a sub-project of Apache’s Lucene in 2008. In 2010, Mahout became a top level project of Apache.

The Mahout project was started by several people involved in the Apache Lucene (open source search) community with an active interest in machine learning and a desire for robust, well-documented, scalable implementations of common machine-learning algorithms for clustering and categorization. The community was initially driven by Ng et al.’s paper “Map-Reduce for Machine Learning on Multicore” (see Resources) but has since evolved to cover much broader machine-learning approaches. Mahout also aims to:

Desired to gain proficiency on Apache Mahout? Explore the blog post
 on Apache Mahout training to become a pro in Apache Mahout.

-Build and support a community of users and contributors such that the code outlives any particular contributor’s involvement or any particular company or university’s funding.

-Focus on real-world, practical use cases as opposed to bleeding-edge research or unproven techniques.

-Provide quality documentation and examples.

Mahout Uses

Collaborative filtering – mines user behavior and makes product recommendations (e.g. Amazon recommendations)

Clustering – takes items in a particular class (such as web pages or newspaper articles) and organizes them into naturally occurring groups, such that items belonging to the same group are similar to each other

Classification – learns from existing categorizations and then assigns unclassified items to the best category

Frequent itemset mining – analyzes items in a group (e.g. items in a shopping cart or terms in a query session) and then identifies which items typically appear together.

Applications of  Mahout Clustering

-Clustering is broadly used in many applications such as market research, pattern recognition, data analysis, and image processing.

-Clustering can help marketers discover distinct groups in their customer basis. And they can characterize their customer groups based on purchasing patterns.

-In the field of biology, it can be used to derive plant and animal taxonomies, categorize genes with similar functionality and gain insight into structures inherent in populations.

-Clustering helps in identification of areas of similar land use in an earth observation database.

-Clustering also helps in classifying documents on the web for information discovery.

-Clustering is used in outlive detection applications such as detection of credit card fraud.

-As a data mining function, Cluster Analysis serves as a tool to gain insight into the distribution of data to observe characteristics of each cluster.

Check out the top Apache Mahout Interview Questions now!

Using Mahout, we can cluster a given set of data. The steps required are as follows:

-Algorithm You need to select a suitable clustering algorithm to group the elements of a cluster.

-Similarity and Dissimilarity You need to have a rule in place to verify the similarity between the newly encountered elements and the elements in the groups.

-Stopping Condition A stopping condition is required to define the point where no clustering is required.


-Taste CF. Taste is an open source project for CF started by Sean Owen on Source Forge and donated to Mahout in 2008.

-Several Map-Reduce enabled clustering implementations, including k-Means, fuzzy k-Means, Canopy, Dirichlet, and Mean-Shift.

-Distributed Naive Bayes and Complementary Naive Bayes classification implementations.

-Distributed fitness function capabilities for evolutionary programming.

-Matrix and vector libraries.

-Examples of all of the above algorithms.

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