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Analyzed 17 days ago. based on code collected 17 days ago.

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
64%
CSS
10%
JavaScript
9%
9 Other
17%

30 Day Summary

Oct 27 2017 — Nov 26 2017

12 Month Summary

Nov 26 2016 — Nov 26 2017
  • 201 Commits
    Down -62 (23%) from previous 12 months
  • 14 Contributors
    Up + 6 (75%) from previous 12 months

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5 users rate this project:
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