K-means is simple, easy to implement and it often works quite well. It's unlikely to be state-of-the-art for any particular problem, but if you just need to do

K-means is simple, easy to implement and it often works quite well. It's unlikely to be state-of-the-art for any particular problem, but if you just need to do some straightforward clustering as part of a larger data-processing pipeline, K-means is a reasonable default choice, at least until you figure out that the clustering step is your bottleneck in terms of overall performance. For example, this paper http://www.stanford.edu/~acoates/papers/coatesng_nntot2012.pdf http://www.stanford.edu/~acoates/papers/coatesng_nntot2012.p... demonstrates that using K-means to learn feature representations for images (i.e., use k-means to cluster small patches of pixels from natural images, then represent images in the basis of cluster centers; repeat this several times to form a "deep" network of feature representations) gives image classification results that are competitive with much more complex / intimidating deep neural network models.