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I think there is something strange: you are plotting recall and precision for different kmeans parameter, but, this parameters doesn't seem related to your precision.
I think you should plot your decision parameter (the one used in confusion matrix), eg. if you want to recognize an apple, and every time you "see" something you say: "this is an apple", your recall is 100% but your precision should be quite low (depending on how many apples you have in your test).
A contrario, if you almost never said "this is an apple", you probably have a good precision, but a low recall. T
he idea of this graph, as I understand it, is to show "how good is your estimation". If I use the analogy of biometric:
How many bad guys are entering in my house if I want to be sure that all good guys are able to enter?
And help the "user" to find a good compromise... This is the basic goal of ROC curves, see the Wikipedia page here.
