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?
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