1. A method performed by a computer system for clustering a plurality of multi-dimensional feature vectors, the feature vectors describing features of spatial-temporal entities, the method comprising: storing in a database of the computer system a plurality of feature vectors describing features of spatial-temporal entities; selecting, by the computer system, a subset of the stored feature vectors; determining, by the computer system, a set of initial seed vectors by applying a principal component analysis (PCA) algorithm to the selected subset of the feature vectors; clustering, by the computer system, the plurality of feature vectors using a k-means clustering algorithm comprising: providing the plurality of initial seed vectors as initial cluster seeds for the k-means clustering algorithm; determining a set of initial clusters of the feature vectors based on the initial cluster seeds using the k-means clustering algorithm; and iteratively refining the set of initial clusters to obtain a final set of clusters using the k-means clustering algorithm, wherein each of the final clusters comprises a subset of the plurality of feature vectors; and storing the final set of clusters in the database.