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Posted to issues@spark.apache.org by "Xiangrui Meng (JIRA)" <ji...@apache.org> on 2015/01/10 03:33:34 UTC
[jira] [Commented] (SPARK-5056) Implementing Clara k-medoids
clustering algorithm for large datasets
[ https://issues.apache.org/jira/browse/SPARK-5056?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14272277#comment-14272277 ]
Xiangrui Meng commented on SPARK-5056:
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[~tmilinovic] We had some discussion in SPARK-4510 about the complexity of k-medoids possible solutions. Does the proposed algorithm have better complexity?
> Implementing Clara k-medoids clustering algorithm for large datasets
> --------------------------------------------------------------------
>
> Key: SPARK-5056
> URL: https://issues.apache.org/jira/browse/SPARK-5056
> Project: Spark
> Issue Type: Improvement
> Components: MLlib
> Reporter: Tomislav Milinovic
> Priority: Minor
> Labels: features
>
> There is a specific k-medoids clustering algorithm for large datasets. The algorithm is called Clara in R, and is fully described in chapter 3 of Finding Groups in Data: An Introduction to Cluster Analysis. by Kaufman, L and Rousseeuw, PJ (1990).
> The algorithm considers sub-datasets of fixed size (sampsize) such that the time and storage requirements become linear in n rather than quadratic. Each sub-dataset is partitioned into k clusters using the same algorithm as in Partinioning around Medoids (PAM).
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