When I first started reading Dr. Dobbs (DDJ) after it launched in 1975, it was a print publication for microcomputer software. I recall how Dr. Dobbs sponsored the annual Jolt Cola award for excellence in software development. My oh my how things have changed! It seems that Dr. Dobbs has kept up with all the fast paced changes in the computing sciences since then. It has survived where other high-flyers of the day like BYTE did not.
I found an excellent article in a recent issue of the “new” Dr. Dobbs all about machine learning using Apache Mahout, a highly scalable machine learning library that enables developers to use optimized algorithms, such as collaborative filtering and random forest decision-tree-based classifiers.
The Dr. Dobbs article demonstrates how Mahout greatly simplifies extracting recommendations and relationships from input data sets. The author Gaston Hillar also looks at setting up Mahuout and running its recommender on a small data sample. This is a great introduction to this powerful software for the big data arena.