SparkAffinityPropagation (homepage)
Affinity Propagation on Spark
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Affinity Propagation (AP), a graph clustering algorithm based on the concept of "message passing" between data points. Unlike clustering algorithms such as k-means or k-medoids, AP does not require the number of clusters to be determined or estimated before running it. AP is developed by Frey and Dueck. Please refer to the paper[1].
Affinity Propagation on Spark implements Affinity Propagation algorithm on cluster computing system Spark. By leveraging computing cluster, you can run this clustering algorithm on large-scale data sets.
[1] Brendan J. Frey; Delbert Dueck (2007). "Clustering by passing messages between data points". Science. 315 (5814): 972-976.
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How to
Include this package in your Spark Applications using:
spark-shell, pyspark, or spark-submit
> $SPARK_HOME/bin/spark-shell --packages viirya:SparkAffinityPropagation:1.0
sbt
If you use the sbt-spark-package plugin, in your sbt build file, add:
spDependencies += "viirya/SparkAffinityPropagation:1.0"
Otherwise,
resolvers += "Spark Packages Repo" at "https://repos.spark-packages.org/" libraryDependencies += "viirya" % "SparkAffinityPropagation" % "1.0"
Maven
In your pom.xml, add:<dependencies> <!-- list of dependencies --> <dependency> <groupId>viirya</groupId> <artifactId>SparkAffinityPropagation</artifactId> <version>1.0</version> </dependency> </dependencies> <repositories> <!-- list of other repositories --> <repository> <id>SparkPackagesRepo</id> <url>https://repos.spark-packages.org/</url> </repository> </repositories>
Releases
Version: 1.0 ( 290dde | zip | jar ) / Date: 2017-07-29 / License: MIT / Scala version: 2.10