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Posted to issues@spark.apache.org by "Apache Spark (JIRA)" <ji...@apache.org> on 2014/11/20 10:21:34 UTC
[jira] [Commented] (SPARK-4510) Add k-medoids Partitioning Around
Medoids (PAM) algorithm
[ https://issues.apache.org/jira/browse/SPARK-4510?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14219163#comment-14219163 ]
Apache Spark commented on SPARK-4510:
-------------------------------------
User 'fjiang6' has created a pull request for this issue:
https://github.com/apache/spark/pull/3382
> Add k-medoids Partitioning Around Medoids (PAM) algorithm
> ---------------------------------------------------------
>
> Key: SPARK-4510
> URL: https://issues.apache.org/jira/browse/SPARK-4510
> Project: Spark
> Issue Type: New Feature
> Components: MLlib
> Reporter: Fan Jiang
> Labels: features
> Original Estimate: 0h
> Remaining Estimate: 0h
>
> PAM (k-medoids) is more robust to noise and outliers as compared to k-means because it minimizes a sum of pairwise dissimilarities instead of a sum of squared Euclidean distances. A medoid can be defined as the object of a cluster, whose average dissimilarity to all the objects in the cluster is minimal i.e. it is a most centrally located point in the cluster.
> The most common realisation of k-medoid clustering is the Partitioning Around Medoids (PAM) algorithm and is as follows:
> Initialize: randomly select (without replacement) k of the n data points as the medoids
> Associate each data point to the closest medoid. ("closest" here is defined using any valid distance metric, most commonly Euclidean distance, Manhattan distance or Minkowski distance)
> For each medoid m
> For each non-medoid data point o
> Swap m and o and compute the total cost of the configuration
> Select the configuration with the lowest cost.
> Repeat steps 2 to 4 until there is no change in the medoid.
> The new feature for MLlib will contain 5 new files
> /main/scala/org/apache/spark/mllib/clustering/PAM.scala
> /main/scala/org/apache/spark/mllib/clustering/PAMModel.scala
> /main/scala/org/apache/spark/mllib/clustering/LocalPAM.scala
> /test/scala/org/apache/spark/mllib/clustering/PAMSuite.scala
> /main/scala/org/apache/spark/examples/mllib/KMedoids.scala
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