Configuring Mahout Clustering Jobs
Presented by Frank Scholten, JTeam
For more than a decade internet search engines have helped users find documents they are looking for. However, what if users aren't looking for anything specific but want a summary of a large document collection and want to be surprised? One solution to this problem is document clustering. Clustering algorithms group documents that have similar content. Real-life examples of clustering are clustered search results of Google news, or tag clouds which group documents under a shared label. Apache Mahout is a framework for scalable machine learning on top of Apache Hadoop and can be used for large scale document clustering. This talk introduces clustering in general and shows you step-by-step how to configure Mahout clustering jobs to create a tag cloud from a document collection. This talk is suitable for people who have some experience with Hadoop and perhaps Mahout. Knowledge of clustering is not required.
Clustering in Mahout
Text pre-processing & analysis
Tag cloud demo
Tips & tricks
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