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dss-orchestrator-framework-appconn from group com.webank.wedatasphere.dss (version 1.0.1)

Group: com.webank.wedatasphere.dss Artifact: dss-orchestrator-framework-appconn
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0 downloads
Artifact dss-orchestrator-framework-appconn
Group com.webank.wedatasphere.dss
Version 1.0.1
Last update 27. February 2022
Organization not specified
URL Not specified
License not specified
Dependencies amount 3
Dependencies dss-orchestrator-core, dss-orchestrator-common, dss-workflow-common,
There are maybe transitive dependencies!

fuzzyUnorderedRuleInduction from group nz.ac.waikato.cms.weka (version 1.0.2)

FURIA: Fuzzy Unordered Rule Induction Algorithm. For details please see: Jens Christian Huehn, Eyke Huellermeier (2009). FURIA: An Algorithm for Unordered Fuzzy Rule Induction. Data Mining and Knowledge Discovery.

Group: nz.ac.waikato.cms.weka Artifact: fuzzyUnorderedRuleInduction
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5 downloads
Artifact fuzzyUnorderedRuleInduction
Group nz.ac.waikato.cms.weka
Version 1.0.2
Last update 29. July 2014
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/fuzzyUnorderedRuleInduction
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
There are maybe transitive dependencies!

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.

Group: nz.ac.waikato.cms.moa Artifact: moa
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54 downloads
Artifact moa
Group nz.ac.waikato.cms.moa
Version 2024.07.0
Last update 18. July 2024
Organization University of Waikato, Hamilton, NZ
URL http://moa.cms.waikato.ac.nz/
License GNU General Public License 3.0
Dependencies amount 19
Dependencies weka-dev, sizeofag, guava, jclasslocator, jshell-scripting, meka, commons-math3, opencsv, commons-cli, commons-io, jfreechart, gson, ssj, JMathPlot, JMathIO, api, slf4j-log4j12, model-zoo, pytorch-engine,
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moa-pom from group nz.ac.waikato.cms.moa (version 2024.07.0)

Group: nz.ac.waikato.cms.moa Artifact: moa-pom
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0 downloads
Artifact moa-pom
Group nz.ac.waikato.cms.moa
Version 2024.07.0

isolationForest from group nz.ac.waikato.cms.weka (version 1.0.2)

Class for building and using a classifier built on the Isolation Forest anomaly detection algorithm. For more information see Fei Tony Liu, Kai Ming Ting and Zhi-Hua Zhou. 2008. Proceedings of the 2008 Eighth IEEE International Conference on Data Mining, pages 413-422.

Group: nz.ac.waikato.cms.weka Artifact: isolationForest
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1 downloads
Artifact isolationForest
Group nz.ac.waikato.cms.weka
Version 1.0.2
Last update 21. January 2016
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/isolationForest
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
There are maybe transitive dependencies!

alternatingModelTrees from group nz.ac.waikato.cms.weka (version 1.0.0)

Grows an alternating model tree by minimising squared error. For more information see "Eibe Frank, Michael Mayo, Stefan Kramer: Alternating Model Trees. In: Proceedings of the ACM Symposium on Applied Computing, Data Mining Track, 2015".

Group: nz.ac.waikato.cms.weka Artifact: alternatingModelTrees
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0 downloads
Artifact alternatingModelTrees
Group nz.ac.waikato.cms.weka
Version 1.0.0
Last update 18. January 2015
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/alternatingModelTrees
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
There are maybe transitive dependencies!

classAssociationRules from group nz.ac.waikato.cms.weka (version 1.0.3)

Class association rules algorithms (including an implementation of the CBA algorithm). For more information see: W. Li, J. Han, J.Pei: CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules. In ICDM'01:369-376,2001. B. Liu, W. Hsu, Y. Ma: Integrating Classification and Association Rule Mining. In KDD'98:80-86,1998.

Group: nz.ac.waikato.cms.weka Artifact: classAssociationRules
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1 downloads
Artifact classAssociationRules
Group nz.ac.waikato.cms.weka
Version 1.0.3
Last update 29. July 2014
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/classAssociationRules
License GNU General Public License 3
Dependencies amount 2
Dependencies weka-dev, predictiveApriori,
There are maybe transitive dependencies!

naiveBayesTree from group nz.ac.waikato.cms.weka (version 1.0.2)

Class for generating a decision tree with naive Bayes classifiers at the leaves. For more information, see Ron Kohavi: Scaling Up the Accuracy of Naive-Bayes Classifiers: A Decision-Tree Hybrid. In: Second International Conference on Knoledge Discovery and Data Mining, 202-207, 1996.

Group: nz.ac.waikato.cms.weka Artifact: naiveBayesTree
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0 downloads
Artifact naiveBayesTree
Group nz.ac.waikato.cms.weka
Version 1.0.2
Last update 26. April 2012
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/naiveBayesTree
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
There are maybe transitive dependencies!

CLOPE from group nz.ac.waikato.cms.weka (version 1.0.2)

Yiling Yang, Xudong Guan, Jinyuan You: CLOPE: a fast and effective clustering algorithm for transactional data. In: Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, 682-687, 2002.

Group: nz.ac.waikato.cms.weka Artifact: CLOPE
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0 downloads
Artifact CLOPE
Group nz.ac.waikato.cms.weka
Version 1.0.2
Last update 26. April 2012
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/CLOPE
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
There are maybe transitive dependencies!

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.

Group: nz.ac.waikato.cms.moa Artifact: moa-kafka
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0 downloads
Artifact moa-kafka
Group nz.ac.waikato.cms.moa
Version 2024.07.0
Last update 18. July 2024
Organization University of Waikato, Hamilton, NZ
URL http://moa.cms.waikato.ac.nz/
License GNU General Public License 3
Dependencies amount 2
Dependencies moa, kafka-clients,
There are maybe transitive dependencies!


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