Download moa-pom JAR file with all dependencies
moa from group nz.ac.waikato.cms.moa (version 2018.6.0)
Massive On-line Analysis is an environment for massive data mining. MOA
provides a framework for data stream mining and includes tools for evaluation
and a collection of machine learning algorithms. Related to the WEKA project,
also written in Java, while scaling to more demanding problems.
31 downloads
moa from group nz.ac.waikato.cms.moa (version 2017.06)
Massive On-line Analysis is an environment for massive data mining. MOA
provides a framework for data stream mining and includes tools for evaluation
and a collection of machine learning algorithms. Related to the WEKA project,
also written in Java, while scaling to more demanding problems.
17 downloads
moa from group nz.ac.waikato.cms.moa (version 2015.11)
Massive On-line Analysis is an environment for massive data mining. MOA
provides a framework for data stream mining and includes tools for evaluation
and a collection of machine learning algorithms. Related to the WEKA project,
also written in Java, while scaling to more demanding problems.
2 downloads
moa from group nz.ac.waikato.cms.moa (version 2016.04)
Massive On-line Analysis is an environment for massive data mining. MOA
provides a framework for data stream mining and includes tools for evaluation
and a collection of machine learning algorithms. Related to the WEKA project,
also written in Java, while scaling to more demanding problems.
2 downloads
moa from group nz.ac.waikato.cms.moa (version 2012.08)
Massive On-line Analysis is an environment for massive data mining. MOA
provides a framework for data stream mining and includes tools for evaluation
and a collection of machine learning algorithms. Related to the WEKA project,
also written in Java, while scaling to more demanding problems.
Group: nz.ac.waikato.cms.moa Artifact: moa
There is no JAR file uploaded. A download is not possible! Please choose another version.
1 downloads
moa from group nz.ac.waikato.cms.moa (version 2014.11)
Massive On-line Analysis is an environment for massive data mining. MOA
provides a framework for data stream mining and includes tools for evaluation
and a collection of machine learning algorithms. Related to the WEKA project,
also written in Java, while scaling to more demanding problems.
Group: nz.ac.waikato.cms.moa Artifact: moa
There is no JAR file uploaded. A download is not possible! Please choose another version.
1 downloads
moa-kafka from group nz.ac.waikato.cms.adams (version 2022.10.17)
Massive On-line Analysis is an environment for massive data mining. MOA provides a framework for data stream mining and includes tools for evaluation and a collection of machine learning algorithms. Related to the WEKA project, also written in Java, while scaling to more demanding problems.
This artifact enables you to stream data to MOA from Kafka.
moa from group nz.ac.waikato.cms.adams (version 2022.10.17)
Massive On-line Analysis is an environment for massive data mining. MOA
provides a framework for data stream mining and includes tools for evaluation
and a collection of machine learning algorithms. Related to the WEKA project,
also written in Java, while scaling to more demanding problems.
ADAMS fork.
moa-kafka from group nz.ac.waikato.cms.moa (version 2024.07.0)
Massive On-line Analysis is an environment for massive data mining. MOA provides a framework for data stream mining and includes tools for evaluation and a collection of machine learning algorithms. Related to the WEKA project, also written in Java, while scaling to more demanding problems.
This artifact enables you to stream data to MOA from Kafka.
moa from group nz.ac.waikato.cms.moa (version 2024.07.0)
Massive On-line Analysis is an environment for massive data mining. MOA
provides a framework for data stream mining and includes tools for evaluation
and a collection of machine learning algorithms. Related to the WEKA project,
also written in Java, while scaling to more demanding problems.
streams-moa from group de.sfb876 (version 0.1.2)
This artifact includes the MOA learning toolbox into the streams Library
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