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K-means has a lot going for it, but it's not very good at recovering "natural" clusters. Like if you have a dataset that you know actually is grouped in a cert
by oofbey 5y ago
K-means has a lot going for it, but it's not very good at recovering "natural" clusters. Like if you have a dataset that you know actually is grouped in a certain way, k-means very often will not discover those clusters. Other more complex clustering algorithms will do a more reliable job. If you have to implement clustering yourself for some reason, k-means' simplicity is great. But how often do you need to write your own algorithm these days?
- twelvechairs 5y agoYes. The visual comparison at [0] gives a good quick overview of clustering algorithms in speed and results. DBSCAN is one with an excellent combination of speed and finding clusters that are human-sensible [0] https://scikit-learn.org/stable/auto_examples/cluster/plot_cluster_comparison.html https://scikit-learn.org/stable/auto_examples/cluster/plot_c...
- hoseja 5y agoYou can still see it failing in that varying-density case.