K-means should work fine even with more dimensions as long as the statistical model that's implicit in k-means is a reasonable model of your data-generating pro

K-means should work fine even with more dimensions as long as the statistical model that's implicit in k-means is a reasonable model of your data-generating process. Specifically: In the "relaxed" form (aka "continuous" form) of k-means that allows cluster centroids to be any coordinate and not just the coordinates of the observations, k-means clustering is equivalent to PCA[1]. PCA works under the assumption that your data generating process can be modeled by a linear factor model with isotropic Gaussian noise[2]. If your data fits these assumptions, or if you can preprocess the dataset accordingly, k-means should be able to give you more than 2 to 3 useful clusters. Machine "learning" usually works best when the dataset is first preprocessed and massaged to expose the hidden structure in a way that can be captured by the algorithms you're using. Kernel SVMs are really mathematically cute with their infinite-dimensional-basis-functions and all that jazz, but I agree that in practice there's a lot more to be gained from understanding the problem at hand and its underlying stochastic process then selecting/designing algorithms accordingly. 1. http://portal.acm.org/citation.cfm?id=1015408 http://portal.acm.org/citation.cfm?id=1015408 2. http://www.robots.ox.ac.uk/~cvrg/hilary2006/ppca.pdf http://www.robots.ox.ac.uk/~cvrg/hilary2006/ppca.pdf