I want to group the outcome of a function into 2 (or 3) categories.
I have a function efficiency=f(weight,speed,#refueling_stops) that takes 3 input parameters and the output tells me how "efficient" a truck is. My goal is to take the most inefficient trucks off the road. However, I don't know which truck to keep and which to reject. In other words, I want to divide all possible output values of my function into the category "keep" or the category "reject" (or the category "between"). Furthermore I have no way to rate how suited my decision was and therefore the point where I draw the line(s) is more or less arbitrary. Nevertheless, I'm looking for a science-based approach to this problem.
Is there a name for this kind of problem?
So far I've stumbled upon clustering (kmeans and natural breaks / Jenks) which is completely new to me. Also I've read that my problem may be similar to converting a color image into black and white (and gray). But I couldn't find out what the current practice for this process is.
Up to now, I've calculated all possible outcomes of my function. The histogram and PDF of the resulting one-dimensional array look like this:
Then I partitioned them into 2 (or 3) categories via R:
library(classInt) x <- read.table("all_possible_outcomes") classIntervals(b, n=2, style = "kmeans") classIntervals(b, n=3, style = "kmeans")
Now I'm curious if this approach to my problem is a current method, or if not, what is the best practice for this? I guess what I'm looking for is some kind of confirmation that it's appropriate to use clustering. If not, what alternatives can you think of?