FAMER_Clustering | research project designed for FAst Multi

by dbs-leipzig Java Version: Current License: Apache-2.0

X-Ray Key Features Code Snippets Community Discussions Vulnerabilities Install Support

kandi X-RAY | FAMER_Clustering Summary

FAMER_Clustering is a Java library. FAMER_Clustering has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.

FAMER is a research project designed for FAst Multi-source Entity Resolution. It is implemented on top of Apache Flink and the graph analytics tool Gradoop. The framework is still highly under development. So the whole code of FAMER is not publicly available yet. This repository provides the new clustering algorithm CLIP as well as the cluster repair algorithm RLIP that we presented in this paper at Extended European Semantic Web Conference in June (ESWC 2018).

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Quality

Security

License

Reuse

Support

FAMER_Clustering has a low active ecosystem.

It has 4 star(s) with 3 fork(s). There are 1 watchers for this library.

It had no major release in the last 6 months.

FAMER_Clustering has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of FAMER_Clustering is current.

Quality

FAMER_Clustering has no bugs reported.

Security

FAMER_Clustering has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

FAMER_Clustering is licensed under the Apache-2.0 License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

FAMER_Clustering releases are not available. You will need to build from source code and install.

Build file is available. You can build the component from source.

Top functions reviewed by kandi - BETA

kandi has reviewed FAMER_Clustering and discovered the below as its top functions. This is intended to give you an instant insight into FAMER_Clustering implemented functionality, and help decide if they suit your requirements.

Reduces a set of vertices into a single cluster
Calculates the sum of degree degrees of a vertex
Compare the cluster collections
Checks if this cluster is the same as another cluster
Perform a flat mapping between two edges
Finds the union between two lists
Performs a reduction on the input stream
Create a cluster from two edges
Performs the actual analysis
Checks if two vertices are merged
Prints the cluster id
Maps a set of vertices to a collector
Performs a flat map across all cluster ids
Executes the given Gelly graph
Checks to see if there are merges in the given cluster
Computes the association degree of all vertices
Calculates the degree of vertices with the highest level of the graph
Reduce the edge values to the output
Reduces a set of vertices
Join two vertices
Reduces all edges to the output
Performs a clustering algorithm on the given graph
Execute the connect

Get all kandi verified functions for this library.

FAMER_Clustering Key Features

No Key Features are available at this moment for FAMER_Clustering.

FAMER_Clustering Examples and Code Snippets

No Code Snippets are available at this moment for FAMER_Clustering.

Community Discussions

No Community Discussions are available at this moment for FAMER_Clustering.Refer to stack overflow page for discussions.

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install FAMER_Clustering

You can download it from GitHub.
You can use FAMER_Clustering like any standard Java library. Please include the the jar files in your classpath. You can also use any IDE and you can run and debug the FAMER_Clustering component as you would do with any other Java program. Best practice is to use a build tool that supports dependency management such as Maven or Gradle. For Maven installation, please refer maven.apache.org. For Gradle installation, please refer gradle.org .

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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