Time complexity of one algorithm cascaded into another?

I am working with random forest for a supervised classification problem,
and I am using the k-means clustering algorithm to split the data at each
node. I am trying to calculate the time complexity for the algorithm. From
what I understand the the time complexity for k-means is O( n * K * I * d )
– n = number of points, K = number of clusters, I = number of iterations, d
= number of attributes. The k, I and d are constants or have an upper
bound, and n is much larger as compared to these three , so i suppose the
complexity is just O(n). The random forest on the other hand is a divide
and conquer approach , so for n instances the complexity is O(n* logn),
though I am not sure about this, correct me if i am wrong. To get the
complexity of the algorithm do i just add these two things?

You received this message because you are subscribed to the Google Groups "elasticsearch" group.
To unsubscribe from this group and stop receiving emails from it, send an email to elasticsearch+unsubscribe@googlegroups.com.
For more options, visit https://groups.google.com/groups/opt_out.