[ https://issues.apache.org/jira/browse/SPARK-2429?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Yu Ishikawa updated SPARK-2429: ------------------------------- Attachment: (was: The Result of Benchmarking a Hierarchical Clustering.pdf) > Hierarchical Implementation of KMeans > ------------------------------------- > > Key: SPARK-2429 > URL: https://issues.apache.org/jira/browse/SPARK-2429 > Project: Spark > Issue Type: New Feature > Components: MLlib > Reporter: RJ Nowling > Assignee: Yu Ishikawa > Priority: Minor > Attachments: The Result of Benchmarking a Hierarchical Clustering.pdf > > > Hierarchical clustering algorithms are widely used and would make a nice > addition to MLlib. Clustering algorithms are useful for determining > relationships between clusters as well as offering faster assignment. > Discussion on the dev list suggested the following possible approaches: > * Top down, recursive application of KMeans > * Reuse DecisionTree implementation with different objective function > * Hierarchical SVD > It was also suggested that support for distance metrics other than Euclidean > such as negative dot or cosine are necessary. -- This message was sent by Atlassian JIRA (v6.3.4#6332) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org