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Project Summary

Apache 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, classfication 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

Tags

algorithms classifiers clustering collaborative_filtering datamining data_mining dimension_reduction distributed distributed_computing hadoop java library machinelearning machine_learning mapreduce recommender regression

In a Nutshell, Apache Mahout...

Apache License 2.0
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These details are provided for information only. No information here is legal advice and should not be used as such.

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This Project has No vulnerabilities Reported Against it

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Languages

Languages?height=75&width=75
Java
77%
Scala
13%
9 Other
10%

30 Day Summary

Feb 11 2018 — Mar 13 2018

12 Month Summary

Mar 13 2017 — Mar 13 2018
  • 235 Commits
    Down -47 (16%) from previous 12 months
  • 17 Contributors
    Up + 8 (88%) from previous 12 months