There's a kMeansModel.clusterCenters() available if u r looking to get the
centers from KMeansModel.
From: SK
To: user@spark.apache.org
Sent: Thursday, February 5, 2015 5:35 PM
Subject: K-Means final cluster centers
Hi,
I am trying to get the final cluster centers after running th
KMeansModel only returns the "cluster centroids".
To get the # of elements in each cluster, try calling kmeans.predict() on each
of the points in the data used to build the model.
See
https://github.com/OryxProject/oryx/blob/master/oryx-app-mllib/src/main/java/com/cloudera/oryx/app/mllib/kmeans/K
Here's the Streaming KMeans from Spark
1.2http://spark.apache.org/docs/latest/mllib-clustering.html#examples-1
Steaming KMeans still needs an initial 'k' to be specified, it then progresses
to come up with an optimal 'k' IIRC.
From: Sean Owen
To: jatinpreet
Cc: "user@spark.apache.org"
Andrew, u would be better off using Mahout's RowSimilarityJob for what u r
trying to accomplish.
1. It does give u pair-wise distances 2. U can specify the Distance measure
u r looking to use 3. There's the old MapReduce impl and the Spark DSL impl
per ur preference.
From: Andrew Mus
Mahout does have a kmeans which can be executed in mapreduce and iterative
modes.
Sent from my iPhone
> On Mar 25, 2014, at 9:25 AM, Prashant Sharma wrote:
>
> I think Mahout uses FuzzyKmeans, which is different algorithm and it is not
> iterative.
>
> Prashant Sharma
>
>
>> On Tue, Mar 2
