H all,
I want to know whether the K means algorithm stops if the data set converges
to stable clusters before reaching the number of iteration that we defined?
As an example if I give 100 as the number of iterations and 5 as the number
of clusters, but if the data set converges to stable 5
Okay. Thanks for the help.
On Sat, Sep 12, 2015 at 1:16 PM, Robineast [via Apache Spark User List] <
ml-node+s1001560n24665...@n3.nabble.com> wrote:
> Yes it does stop if the algorithm converges in less than the specified
> tolerance. You have a parameter to the Kmeans constructor called epsilon
Hi all,
I am currently working on some K means clustering project. I want to get the
distances of each data point to it's cluster center after building the K
means model. Currently I get the cluster centers of each data point by
sending the JavaRDD which includes all the data points to K means
Hi all,
I have a dataset which consist of large number of features(columns). It is
in csv format. So I loaded it into a spark dataframe. Then I converted it
into a JavaRDDRow Then using a spark transformation I converted that into
JavaRDDString[]. Then again converted it into a JavaRDDdouble[].
We can get cluster centers in K means clustering. Like wise is there any
method in spark to get the cluster radius?
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