1. A method for identifying clusters within a collection of data entities, the method comprising: defining a metric space over the data entities, a distance function of the metric space satisfying a triangle inequality; determining, based on the distance function of the metric space, a value for a number of clusters that minimizes a number of data bits used to define a model of the collection of data entities, such that the model describes the collection of data entities using a minimum description length (MDL), wherein the MDL corresponds to a length of a description of the model of the collection of data entities; assigning data entities of the collection of data entities to clusters of the number of clusters, the number of clusters corresponding to the value that minimizes the number of data bits used to define the model of the collection of data entities; generating a count for the number of data entities in the clusters; and ranking the clusters according to the number of data entities assigned to each cluster, such that a cluster with the largest number of data entities represents a geographically-referenced most popular cluster, wherein the determining further comprises determining a median data entity for each of the clusters, and the determining of the value for the number of clusters that minimizes the number of data bits used to define the model of the collection is further based on the median data entity for each of the clusters, and further wherein the collection of data entities comprises polygonal tracks, a polygonal track beginning at a geographical start point and the polygonal track ending at a geographical terminal point, and the determining of the median data entity is refined by removing a vertex of a polygonal track.