machine learning - Clustering algorithm for gps data -


i have data set consisting of gps coordinates points on particular city (let's take san francisco example). want cluster coordinates groups such in image:

enter image description here

should use k-means or dbscan or other clustering algorithm? should find clusters first , find border points draw boundaries?

the example partitioning showed looks more forced quantization me clustering based on structure in data set.

which algorithm choose depends on

  • your data (not data type, actual distribution of values have)
  • your needs (what want get, algorithms built based on different desires)

but don't have data, , don't know desires. except sketch did looks more k-means quantization density based clustering. except k-means minimizes variance, , can't handle latitude, longitude well. can project data appropriate utm zone though; k-means should work.


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