Hi Sean,
Thanks for the quick answer. I have not realized that I can make an
RDD[Vector] with eg.
val dataSet = sparkContext.makeRDD(List(Vectors.dense(10.0,20.0),
Vectors.dense(20.0,30.0)))
Using this KMeans.train works as it should.
So my bad. Thanks again!
Attila
2015-02-15 17:29 GMT+01:00
Clustering operates on a large number of n-dimensional vectors. That
seems to be what you are describing, and that is what the MLlib API
accepts. What are you expecting that you don't find?
Did you have a look at the KMeansModel that this method returns? it
has a "clusterCenters" method that gives
Dear Spark User List,
I'm fairly new to Spark, trying to use it for multi-dimensional clustering
(using the k-means clustering from MLib). However, based on the examples
the clustering seems to work only for a single dimension (KMeans.train()
accepts an RDD[Vector], which is a vector of doubles -
