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jung-parent from group com.northdata.jung (version 2.2.0)
JUNG the Java Universal Network/Graph Framework--is a software
library that provides a common and extendible language for the
modeling, analysis, and visualization of data that can be
represented as a graph or network. It is written in Java, which
allows JUNG-based applications to make use of the extensive
built-in capabilities of the Java API, as well as those of other
existing third-party Java libraries. The JUNG architecture is
designed to support a variety of representations of entities and
their relations, such as directed and undirected graphs,
multi-modal graphs, graphs with parallel edges, and hypergraphs.
It provides a mechanism for annotating graphs, entities, and
relations with metadata. This facilitates the creation of
analytic tools for complex data sets that can examine the
relations between entities as well as the metadata attached to
each entity and relation. The current distribution of JUNG
includes implementations of a number of algorithms from graph
theory, data mining, and social network analysis, such as
routines for clustering, decomposition, optimization, random
graph generation, statistical analysis, and calculation of
network distances, flows, and importance measures (centrality,
PageRank, HITS, etc.). JUNG also provides a visualization
framework that makes it easy to construct tools for the
interactive exploration of network data. Users can use one of
the layout algorithms provided, or use the framework to create
their own custom layouts. In addition, filtering mechanisms are
provided which allow users to focus their attention, or their
algorithms, on specific portions of the graph.
Group: com.northdata.jung Artifact: jung-parent
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Artifact jung-parent
Group com.northdata.jung
Version 2.2.0
Last update 18. September 2020
Organization not specified
URL http://jrtom.github.io/jung/
License The BSD License
Dependencies amount 0
Dependencies No dependencies
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Group com.northdata.jung
Version 2.2.0
Last update 18. September 2020
Organization not specified
URL http://jrtom.github.io/jung/
License The BSD License
Dependencies amount 0
Dependencies No dependencies
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openimaj from group org.openimaj (version 1.3.10)
OpenIMAJ (Open Intelligent Multimedia in Java) is a collection of libraries and tools for multimedia analysis written in the Java programming language. OpenIMAJ intends to be the first truly complete multimedia analysis library and contains modules for analysing images, videos, text, audio and even webpages. The OpenIMAJ image and video analysis and feature extraction modules contain methods for processing visual content and extracting state-of-the-art features, including SIFT. The OpenIMAJ clustering and nearest-neighbour libraries contain efficient, multi-threaded implementations of clustering algorithms including Hierarchical K-Means and Approximate K-Means. The clustering library makes it possible to easily create visual-bag-of-words representations for images and video with very large vocabularies. The text-analysis modules contain implementations of a statistical language classifier and low-level processing pipeline. A number of modules deal with content creation, including interactive slideshows and animations. The hardware integration modules allow cross-platform integration with devices including webcams, the Microsoft Kinect, and even devices such as GPS's. OpenIMAJ also incorporates a number of tools to enable extremely-large-scale multimedia analysis using a distributed computing approach based on Apache Hadoop.
Group: org.openimaj Artifact: openimaj
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Artifact openimaj
Group org.openimaj
Version 1.3.10
Last update 09. February 2020
Organization The University of Southampton
URL http://www.openimaj.org
License New BSD
Dependencies amount 0
Dependencies No dependencies
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Group org.openimaj
Version 1.3.10
Last update 09. February 2020
Organization The University of Southampton
URL http://www.openimaj.org
License New BSD
Dependencies amount 0
Dependencies No dependencies
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rng from group de.cit-ec.ml (version 1.0.0)
This is an implementation of the Neural Gas algorithm on
distance data (Relational Neural Gas) for unsupervised clustering.
We recommend that you use the functions provided by the RelationalNeuralGas
class for your purposes. All other classes and functions are utilities which
are used by this central class. In particular, you can use RelationalNeuralGas.train()
to obtain a RNGModel (i.e. a clustering of your data), and subsequently
you can use RelationalNeuralGas.getAssignments() to obtain the resulting
cluster assignments, and RelationalNeuralGas.classify() to cluster new points
which are not part of the training data set.
The underlying scientific work is summarized nicely in the dissertation
"Topographic Mapping of Dissimilarity Datasets" by Alexander Hasenfuss
(2009).
The basic properties of an Relational Neural Gas algorithm are the following:
1.) It is relational: The data is represented only in terms of a pairwise
distance matrix.
2.) It is a clustering method: The algorithm provides a clustering model,
that is: After calculation,
each data point should be assigned to a cluster (for this package here we
only consider hard clustering, that is: each data point is assigned to
exactly one cluster).
3.) It is a vector quantization method: Each cluster corresponds to a
prototype, which is in the center of the
cluster and data points are assigned to the cluster if and only if they are
closest to this particular prototype.
4.) It is rank-based: The updates of the prototypes depend only on
the distance ranking, not on the absolute value of the distances.
Artifact rng
Group de.cit-ec.ml
Version 1.0.0
Last update 26. January 2018
Organization not specified
URL https://gitlab.ub.uni-bielefeld.de/bpaassen/relational_neural_gas
License The GNU General Public License, Version 3
Dependencies amount 0
Dependencies No dependencies
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Group de.cit-ec.ml
Version 1.0.0
Last update 26. January 2018
Organization not specified
URL https://gitlab.ub.uni-bielefeld.de/bpaassen/relational_neural_gas
License The GNU General Public License, Version 3
Dependencies amount 0
Dependencies No dependencies
There are maybe transitive dependencies!
jung-parent from group net.sf.jung (version 2.1.1)
JUNG the Java Universal Network/Graph Framework--is a software
library that provides a common and extendible language for the
modeling, analysis, and visualization of data that can be
represented as a graph or network. It is written in Java, which
allows JUNG-based applications to make use of the extensive
built-in capabilities of the Java API, as well as those of other
existing third-party Java libraries. The JUNG architecture is
designed to support a variety of representations of entities and
their relations, such as directed and undirected graphs,
multi-modal graphs, graphs with parallel edges, and hypergraphs.
It provides a mechanism for annotating graphs, entities, and
relations with metadata. This facilitates the creation of
analytic tools for complex data sets that can examine the
relations between entities as well as the metadata attached to
each entity and relation. The current distribution of JUNG
includes implementations of a number of algorithms from graph
theory, data mining, and social network analysis, such as
routines for clustering, decomposition, optimization, random
graph generation, statistical analysis, and calculation of
network distances, flows, and importance measures (centrality,
PageRank, HITS, etc.). JUNG also provides a visualization
framework that makes it easy to construct tools for the
interactive exploration of network data. Users can use one of
the layout algorithms provided, or use the framework to create
their own custom layouts. In addition, filtering mechanisms are
provided which allow users to focus their attention, or their
algorithms, on specific portions of the graph.
Group: net.sf.jung Artifact: jung-parent
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Artifact jung-parent
Group net.sf.jung
Version 2.1.1
Last update 07. September 2016
Organization not specified
URL http://jrtom.github.io/jung/
License The BSD License
Dependencies amount 0
Dependencies No dependencies
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Group net.sf.jung
Version 2.1.1
Last update 07. September 2016
Organization not specified
URL http://jrtom.github.io/jung/
License The BSD License
Dependencies amount 0
Dependencies No dependencies
There are maybe transitive dependencies!
jung2 from group net.sf.jung (version 2.0.1)
JUNG the Java Universal Network/Graph Framework--is a software
library that provides a common and extendible language for the
modeling, analysis, and visualization of data that can be
represented as a graph or network. It is written in Java, which
allows JUNG-based applications to make use of the extensive
built-in capabilities of the Java API, as well as those of other
existing third-party Java libraries. The JUNG architecture is
designed to support a variety of representations of entities and
their relations, such as directed and undirected graphs,
multi-modal graphs, graphs with parallel edges, and hypergraphs.
It provides a mechanism for annotating graphs, entities, and
relations with metadata. This facilitates the creation of
analytic tools for complex data sets that can examine the
relations between entities as well as the metadata attached to
each entity and relation. The current distribution of JUNG
includes implementations of a number of algorithms from graph
theory, data mining, and social network analysis, such as
routines for clustering, decomposition, optimization, random
graph generation, statistical analysis, and calculation of
network distances, flows, and importance measures (centrality,
PageRank, HITS, etc.). JUNG also provides a visualization
framework that makes it easy to construct tools for the
interactive exploration of network data. Users can use one of
the layout algorithms provided, or use the framework to create
their own custom layouts. In addition, filtering mechanisms are
provided which allow users to focus their attention, or their
algorithms, on specific portions of the graph.
Group: net.sf.jung Artifact: jung2
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Artifact jung2
Group net.sf.jung
Version 2.0.1
Last update 24. January 2010
Organization not specified
URL http://jung.sourceforge.net/site
License The BSD License
Dependencies amount 0
Dependencies No dependencies
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Group net.sf.jung
Version 2.0.1
Last update 24. January 2010
Organization not specified
URL http://jung.sourceforge.net/site
License The BSD License
Dependencies amount 0
Dependencies No dependencies
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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
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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
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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
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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
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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
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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
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