Download JAR files tagged by itemset with all dependencies
fim-model from group be.uantwerpen.adrem (version 0.1)
This package provides the basic model for Frequent Itemset Mining.
Artifact fim-model
Group be.uantwerpen.adrem
Version 0.1
Last update 21. September 2015
Organization University of Antwerp - ADReM Research Group
URL https://gitlab.com/adrem/fim-model
License GNU AFFERO GENERAL PUBLIC LICENSE, Version 3
Dependencies amount 0
Dependencies No dependencies
There are maybe transitive dependencies!
Group be.uantwerpen.adrem
Version 0.1
Last update 21. September 2015
Organization University of Antwerp - ADReM Research Group
URL https://gitlab.com/adrem/fim-model
License GNU AFFERO GENERAL PUBLIC LICENSE, Version 3
Dependencies amount 0
Dependencies No dependencies
There are maybe transitive dependencies!
elki-itemsets from group io.github.elki-project (version 0.8.0)
ELKI - Itemset Mining – Open-Source Data-Mining Framework with Index Acceleration
Artifact elki-itemsets
Group io.github.elki-project
Version 0.8.0
Last update 08. October 2022
Organization ELKI Development Team
URL https://elki-project.github.io/
License GNU Affero General Public License (AGPL) version 3.0
Dependencies amount 1
Dependencies elki-core,
There are maybe transitive dependencies!
Group io.github.elki-project
Version 0.8.0
Last update 08. October 2022
Organization ELKI Development Team
URL https://elki-project.github.io/
License GNU Affero General Public License (AGPL) version 3.0
Dependencies amount 1
Dependencies elki-core,
There are maybe transitive dependencies!
elki-itemsets from group de.lmu.ifi.dbs.elki (version 0.7.5)
ELKI - Itemset Mining – Open-Source Data-Mining Framework with Index Acceleration
Artifact elki-itemsets
Group de.lmu.ifi.dbs.elki
Version 0.7.5
Last update 15. February 2019
Organization ELKI Development Team
URL https://elki-project.github.io/
License GNU Affero General Public License (AGPL) version 3.0
Dependencies amount 1
Dependencies elki-core,
There are maybe transitive dependencies!
Group de.lmu.ifi.dbs.elki
Version 0.7.5
Last update 15. February 2019
Organization ELKI Development Team
URL https://elki-project.github.io/
License GNU Affero General Public License (AGPL) version 3.0
Dependencies amount 1
Dependencies elki-core,
There are maybe transitive dependencies!
fim-measures from group be.uantwerpen.adrem (version 0.1)
Interestingness measures for frequent itemset and association rule mining tasks.
Artifact fim-measures
Group be.uantwerpen.adrem
Version 0.1
Last update 30. January 2016
Organization not specified
URL https://gitlab.com/adrem/fim-measure
License GNU AFFERO GENERAL PUBLIC LICENSE, Version 3
Dependencies amount 1
Dependencies fim-model,
There are maybe transitive dependencies!
Group be.uantwerpen.adrem
Version 0.1
Last update 30. January 2016
Organization not specified
URL https://gitlab.com/adrem/fim-measure
License GNU AFFERO GENERAL PUBLIC LICENSE, Version 3
Dependencies amount 1
Dependencies fim-model,
There are maybe transitive dependencies!
generalizedSequentialPatterns from group nz.ac.waikato.cms.weka (version 1.0.2)
Class implementing a GSP algorithm for discovering sequential patterns in a sequential data set. The attribute identifying the distinct data sequences contained in the set can be determined by the respective option. Furthermore, the set of output results can be restricted by specifying one or more attributes that have to be contained in each element/itemset of a sequence. For further information see: Ramakrishnan Srikant, Rakesh Agrawal (1996). Mining Sequential Patterns: Generalizations and Performance Improvements.
Group: nz.ac.waikato.cms.weka Artifact: generalizedSequentialPatterns
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Artifact generalizedSequentialPatterns
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/generalizedSequentialPatterns
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
There are maybe transitive dependencies!
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/generalizedSequentialPatterns
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
There are maybe transitive dependencies!
mahout from group org.apache.mahout (version 14.1)
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, classification 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. Scalable to support your business case. Mahout is distributed under a commercially
friendly Apache Software license. Scalable community. The goal of Mahout is to build a vibrant, responsive,
diverse community to facilitate discussions not only on the project itself but also on potential use cases. Come
to the mailing lists to find out more. Currently Mahout supports mainly four use cases: Recommendation mining
takes users' behavior and from that tries to find items users might like. Clustering takes e.g. text documents
and groups them into groups of topically related documents. Classification learns from existing categorized
documents what documents of a specific category look like and is able to assign unlabelled documents to the
(hopefully) correct category. Frequent itemset mining takes a set of item groups (terms in a query session,
shopping cart content) and identifies, which individual items usually appear together.
Group: org.apache.mahout Artifact: mahout
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Artifact mahout
Group org.apache.mahout
Version 14.1
Last update 16. July 2020
Organization The Apache Software Foundation
URL http://mahout.apache.org
License Apache License, Version 2.0
Dependencies amount 0
Dependencies No dependencies
There are maybe transitive dependencies!
Group org.apache.mahout
Version 14.1
Last update 16. July 2020
Organization The Apache Software Foundation
URL http://mahout.apache.org
License Apache License, Version 2.0
Dependencies amount 0
Dependencies No dependencies
There are maybe transitive dependencies!
mahout-eclipse-support from group org.apache.mahout (version 0.5)
Artifact mahout-eclipse-support
Group org.apache.mahout
Version 0.5
Last update 28. May 2011
Organization not specified
URL Not specified
License not specified
Dependencies amount 0
Dependencies No dependencies
There are maybe transitive dependencies!
Group org.apache.mahout
Version 0.5
Last update 28. May 2011
Organization not specified
URL Not specified
License not specified
Dependencies amount 0
Dependencies No dependencies
There are maybe transitive dependencies!
mahout-parent from group org.apache.mahout (version 0.3)
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. Scalable to support your business case. Mahout is distributed under a commercially friendly Apache Software license. Scalable community. The goal of Mahout is to build a vibrant, responsive, diverse community to facilitate discussions not only on the project itself but also on potential use cases. Come to the mailing lists to find out more. Currently Mahout supports mainly four use cases: Recommendation mining takes users' behavior and from that tries to find items users might like. Clustering takes e.g. text documents and groups them into groups of topically related documents. Classification learns from exisiting categorized documents what documents of a specific category look like and is able to assign unlabelled documents to the (hopefully) correct category. Frequent itemset mining takes a set of item groups (terms in a query session, shopping cart content) and identifies, which individual items usually appear together.
Group: org.apache.mahout Artifact: mahout-parent
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Artifact mahout-parent
Group org.apache.mahout
Version 0.3
Last update 12. March 2010
Organization The Apache Software Foundation
URL http://lucene.apache.org/mahout
License The Apache Software License, Version 2.0
Dependencies amount 0
Dependencies No dependencies
There are maybe transitive dependencies!
Group org.apache.mahout
Version 0.3
Last update 12. March 2010
Organization The Apache Software Foundation
URL http://lucene.apache.org/mahout
License The Apache Software License, Version 2.0
Dependencies amount 0
Dependencies No dependencies
There are maybe transitive dependencies!
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