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I need help selecting or creating a clustering algorithm according to certain criteria. Imagine you are managing newspaper delivery persons. You have a set of street addresses, each of which is geocoded. You want to cluster the addresses so that each cluster is assigned to a delivery person. The number of delivery persons, or clusters, is not fixed. If needed, I can always hire more delivery persons, or lay them off. Each cluster should have about the same number of addresses. However, a cluster may have less addresses if a cluster's addresses are more spread out. (Worded another way: minimum number of clusters where each cluster contains a maximum number of addresses, and any address within cluster must be separated by a maximum distance.) For bonus points, when the data set is altered (address added or removed), and the algorithm is re-run, it would be nice if the clusters remained as unchanged as possible (ie. this rules out simple k-means clustering which is random in nature). Otherwise the delivery persons will go crazy. So... ideas? UPDATE The street network graph, as described in Arachnid's answer, is not available.

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