Condensed Nearest Neighbor Explanation
Z is not the array of misclassified points.  Try working through an example, and be careful and precise when making statements like that; while you can talk about which points are classified by a particular classifier, that will depend on the classifier.  So, when there are multiple classifiers floating around (or multiple training sets floating around), you need to specify which classifier you are referring to.  I suspect this is what is tripping you up: something that is misclassified with one training set might be correctly classified with another training set.

The key guarantee of this procedure is: if you use Z as your training set for a nearest-neighbor classifier, then this NN classifier will have 100% accuracy.  Try working through an example to see why.  (It iteratively grows the training set as needed to ensure that every point will be classified correctly by that NN classifier.)

As always, when you are confused, start by working through a small example with a few points (two, three, four), simulating the algorithm by hand, with pencil and paper.  That will often be very effective at building intuition -- and in this case it should be sufficient to understand what is going on.