[P] KMin - Clustering algorithm • r/MachineLearning

In cases where an L1-norm or L-infinity norm better describe distance, this could be useful. For example, dealing with a square-grid pattern in city streets may yield better results when using scaled geographic coordinates. K-means is effectively an algorithm that considers all points around each cluster center to be distributed around that point according to an N-dimensional normal distribution with a constant diagonal and no correlations. This works well when your clusters can be approximated to be roughly a circular shape (which corresponds to the L2 norm of Euclidean space). If your cluster patterns were squares, cubes or hypercubes, this would work better for an L-infinity norm, and likewise diamond shapes would work better with an L1-norm.