13. A machine-implemented method, comprising: receiving a first set of scan data that includes information regarding wireless access points available to each device of the first group; computing a set of statistics for each of multiple subsets of devices of the first group based on the first set of scan data; classifying each of the subsets of devices in one of multiple proximity classes; training a model to classify the subsets of devices in the respective proximity classes based on the respective sets of statistics; receiving a second set of scan data that includes information regarding wireless access points available to each device of a second group; computing the set of statistics for the second group of devices based on the second set of scan data; and classifying the second group of devices in one of the proximity classes based on the set of statistics of the second group and the model.
13. A machine-implemented method, comprising: receiving a first set of scan data that includes information regarding wireless access points available to each device of the first group; computing a set of statistics for each of multiple subsets of devices of the first group based on the first set of scan data; classifying each of the subsets of devices in one of multiple proximity classes; training a model to classify the subsets of devices in the respective proximity classes based on the respective sets of statistics; receiving a second set of scan data that includes information regarding wireless access points available to each device of a second group; computing the set of statistics for the second group of devices based on the second set of scan data; and classifying the second group of devices in one of the proximity classes based on the set of statistics of the second group and the model. 16. The machine-implemented method of claim 13 , wherein the computing a set of statistics includes computing the set of statistics for pairs of devices of the first group.