Apache Mahout
· Mahout's goal is to build scalable machine learning libraries. With scalable we mean: Scalable to
reasonably large data sets. Our core algorithms for clustering, classification and batch based collaborative
filtering are implemented on top of Apache Hadoop using the map/reduce paradigm. However we do not restrict
contributions to Hadoop based implementations: Contributions that run on a single node or on a non-Hadoop
cluster are welcome as well. The core libraries are highly optimized to allow for good performance also for
non-distributed algorithms. Scalable to support your business case. Mahout is distributed under a commercially
friendly Apache Software license. Scalable community. The goal of Mahout is to build a vibrant, responsive,
diverse community to facilitate discussions not only on the project itself but also on potential use cases. Come
to the mailing lists to find out more. Currently Mahout supports mainly four use cases: Recommendation mining
takes users' behavior and from that tries to find items users might like. Clustering takes e.g. text documents
and groups them into groups of topically related documents. Classification learns from existing categorized
documents what documents of a specific category look like and is able to assign unlabelled documents to the
(hopefully) correct category. Frequent itemset mining takes a set of item groups (terms in a query session,
shopping cart content) and identifies, which individual items usually appear together.
Download JD-GUI to open JAR file and explore Java source code file (.class .java)
Click menu "File → Open File..." or just drag-and-drop the JAR file in the JD-GUI window mahout-14.1.jar file.
Once you open a JAR file, all the java classes in the JAR file will be displayed.
