What is Apache Mahout and How does it works?

What is Apache Mahout and How does it works?

Apache Mahout(TM) is a distributed linear algebra framework and mathematically expressive Scala DSL designed to let mathematicians, statisticians, and data scientists quickly implement their own algorithms. Apache Spark is the recommended out-of-the-box distributed back-end, or can be extended to other distributed backends.

Table of Content

  1. What Is Meant By Apache Mahout?
  2. Overview Of Apache Mahout

What is meant by Apache Mahout?

Apache Mahout is a project of the Apache Software Foundation that is implemented on top of Apache Hadoop and uses the MapReduce paradigm. It is also used for the implementation of scalable and distributed machine learning algorithms that are focused in the areas of clustering, collaborative filtering, and classification. Mahout includes Java libraries for general math algorithms and operations focused on statistics and linear algebra, as well as primitive Java collections. Apache Mahout is entirely about machine learning and this project aims to create a powerful tool to build intelligent applications faster and easier.

It used to be the exclusive domain of academics and corporations with big research budgets, but in today’s data-driven world, there is a growing need for intelligent applications that learn from data and user data.

Apache Mahout is used to find similarities in large data sets or tag large amounts of web content with machine-learning techniques such as clustering, classification, and collaborative filtering.

Overview of Apache Mahout

About Lucien

Lucene is an API and a project in Apache, which helps to implement a search engine within your application. It supports searching in heterogeneous data sources. With Lucene, you can search through MySQL databases, raw content, XML content, Excel content, or any data format. So basically, it provides all kinds of text analytics. On top of that, it offers a very advanced search framework so you can take advantage of Apache Lucene and start using it to implement search engines in your application. Apache Lucene gives you fast search results even on massive data search. Lucene API offers you to do quick text analytics by searching over heterogeneous data types. Leucine provides advanced implementation of search, text mining and information retrieval techniques. In the universe of computer science, these concepts are associated with machine learning techniques, such as clustering and, to an extent, classification. As a result, some of the Lucene committers' work that fell more into these machine learning areas was turned into its own sub-project called Mahaut.

Leucine with solar

By integrating Lucene with Solr, which is another product of Lucene, you can manage distributed indexes using Solr.

  • Solr is able to run your queries in parallel in a distributed index. He's a combination of both Leucine and Solar.
  • Solar is basically a server type of system.
  • It provides distributed sequencing capability over the top of Lucene.

Mahout Originated from Lucien

Apache Lucene is the core for Mahout's genesis. In 2008, Lucene had some algorithms to do clustering by default. Since it had some built-in analytics capabilities, like clustering, when they actually added the recommendation engine on top of the search features, they started a new project called Mahaut. It became a sub-level project of Apache. Later, Mahout absorbed an open-source collaborative filtering project, Taste.

Machine Learning on the World Wide Web

Machine learning has taken over the World Wide Web for various use cases, especially talking about recommendations and clustering classification. All data science related problems arise on the World Wide Web, and Machine Learning today complements the Web by providing solutions for the same.

Mahout A Scalable Machine Learning Implementation

The real feature of Mahout is that it is highly scalable as it runs algorithms on top of Hadoop environment with support of MapReduce and HDFS. Compared to other traditional machine learning tools, such as R, Veeka, Octave, etc., Mahaut is a very good complement. When you are working with massive data-sets, a traditional application running algorithms over such large amounts of data is most likely to fail. This is where the Mahavat gets its importance, even if it has the ability to run in standalone mode.

Functionality for today’s common machine learning tasks

Mahout has functionality for most machine learning tasks usually required. Several machine learning techniques have already been part of Mahout and research is underway to add more. There are a lot of algorithms that have been migrated. Sooner or later, you can see the latest release of Mahout. Mahout 1.0. Currently, the latest version of Mahout is Mahout 0.8. In Mahout 0.8, there are some algorithms that have not really been optimized. The Mahaut team plans to remove several algorithms that are not supported. They will keep only those algorithms, which have been supported and optimized and implemented very well for 1.0. They also have plans to add more support for future algorithms. They're open to suggestions from outside. So, you can even contribute to the Mahaut project to add any algorithm of your choice. For example, for example if you want to add an artificial neural network support, Mahout will definitely be open to take your suggestion to add such algorithms.

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