What is the right approach and clustering algorithm for geolocation clustering?I'm using the following code to cluster geolocation coordinates:import numpy as npimport matplotlib.pyplot as pltfrom scipy.cluster.vq import kmeans2, whitencoordinates= np.array([           [lat, long],           [lat, long],            ...           [lat, long]           ])x, y = kmeans2(whiten(coordinates), 3, iter = 20)  plt.scatter(coordinates[:,0], coordinates[:,1], c=y);plt.show()Is it right to use K-means for geolocation clustering, as it uses Euclidean distance, and not Haversine formula as a distance function?
Clustering geo location coordinates (lat,long pairs)
machine learning;python;clustering;k means;geospatial
K-means should be right in this case. Since k-means tries to group based solely on euclidean distance between objects you will get back clusters of locations that are close to each other. To find the optimal number of clusters you can try making an 'elbow' plot of the within group sum of square distance. This may be helpful (http://nbviewer.ipython.org/github/nborwankar/LearnDataScience/blob/master/notebooks/D3.%20K-Means%20Clustering%20Analysis.ipynb)