Classifiying documents using Naive Bayes on Apache Spark / MLlib | Chimpler
In recent years, Apache Spark has gained in popularity as a faster alternative to Hadoop and it reached a major milestone last month by releasing the production ready version 1.0.0. It claims to be up to a 100 times faster by leveraging the distributed memory of the cluster and by not being tied to the multi stage execution of Map/Reduce. Like Hadoop, it offers a similar ecosystem with a database (Shark SQL), a machine learning library (MLlib), a graph library (GraphX) and many other tools built on top of Spark. Finally Spark integrates well with Scala and one can manipulate distributed collections just like regular Scala collections and Spark will take care of distributing the processing to the different workers. In this post, we describe how we used Spark / MLlib to classify HTML documents using the popular Reuters 21578 collection of documents that appeared on Reuters newswire in 1987 as a training set. The Reuters collection can be obtained from http://archive.ics.uci.Read full article from Classifiying documents using Naive Bayes on Apache Spark / MLlib | Chimpler
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