An unsupervised clustering algorithm
“K” stands for number of clusters, it is typically a user input to the algorithm; some criteria can be used to automatically estimate K
It is an approximation to an NP-hard combinatorial optimization problem
K-means algorithm is iterative in nature
It converges, however only a local minimum is obtained
Works only for numerical data
Easy to implement