APACHE MAHOUT

Apache Mahout is a project of the Apache Software Foundation which is implemented on top of Apache Hadoop and uses the MapReduce paradigm. It is also used to create implementations of scalable and distributed machine learning algorithms that are focused in the areas of clustering, collaborative filtering and classification. Mahout contains Java libraries for common math algorithms and operations focused on statistics and linear algebra, as well as primitive Java collections. Mahout’s goal is to build

  • Scalable machine learning libraries, Scalable enough to reasonably large data sets.
  • Scalable to support your business case, Mahout is distributed under commercial friendly Apache software license.
  • Scalable community.
Mahout supports four main data science use cases:
  • Collaborative filtering – mines user behaviour 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

Who Should Do ?

B.Tech, M.Tech, PHD Researchers, Other Professional Researchers.


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