My experience has been the opposite, K-Means clustering is generally pretty useless in the real world with real data.
In general clustering algorithms tend not to be very useful because they are ultimately just very poorly defined latent variable models. Most people starting out with clustering don't even understand that they're making the assumption that K latent variables are responsible for generating the observed data.
K-means in particular has loads of problems for practical applications: the final location of the means is heavily dependent on the initial locations, the final location can also be heavily dependent on the particular dataset used (try bootstrap resampling to get variance of your means), assumes the data rests on a Euclidean surface, making geometric assumptions about high dimensional spaces is almost always a bad a bad idea (things being close by in Euclidean terms in high dimensional spaces often misses things that are nearly identical except on feature that is quite different).
With over a decade of modeling experience and building data science products I have never seen a product ship that relies on k-means and never seen analysis that isn't coming form someone junior in the field that relies on it.
K-means is a great demo the expectation maximization algorithm, which is a useful tool to understand, but generally I would avoid k-means, and clustering in general unless you have an explicit understanding of why you are doing it. Even then, there is probably a more stable and appropriate latent variable model you could use.
In general clustering algorithms tend not to be very useful because they are ultimately just very poorly defined latent variable models. Most people starting out with clustering don't even understand that they're making the assumption that K latent variables are responsible for generating the observed data.
K-means in particular has loads of problems for practical applications: the final location of the means is heavily dependent on the initial locations, the final location can also be heavily dependent on the particular dataset used (try bootstrap resampling to get variance of your means), assumes the data rests on a Euclidean surface, making geometric assumptions about high dimensional spaces is almost always a bad a bad idea (things being close by in Euclidean terms in high dimensional spaces often misses things that are nearly identical except on feature that is quite different).
With over a decade of modeling experience and building data science products I have never seen a product ship that relies on k-means and never seen analysis that isn't coming form someone junior in the field that relies on it.
K-means is a great demo the expectation maximization algorithm, which is a useful tool to understand, but generally I would avoid k-means, and clustering in general unless you have an explicit understanding of why you are doing it. Even then, there is probably a more stable and appropriate latent variable model you could use.