kmeans algorithm is very popular and used in a variety of applications such as market segmentation, document clustering, image segmentation and image compression, etc.
The approach kmeans follows to solve the problem is called Expectation-Maximization.
Since clustering algorithms including kmeans use distance-based measurements to determine the similarity between data points, it’s recommended to standardize the data to have a mean of zero and a standard deviation of one since almost always the features in any dataset would have different units of measurements such as age vs income. Given kmeans iterative nature and the random initialization of centroids at the start of the algorithm, different initializations may lead to different clusters since kmeans algorithm may stuck in a local optimum and may not converge to global optimum. Therefore, it’s recommended to run the algorithm using different initializations of centroids and pick the results of the run that that yielded the lower sum of squared distance.
init{‘k-means++’, ‘random’, ndarray, callable}, default=’k-means++’
Method for initialization:
'k-means++': selects initial cluster centers for k-mean clustering in a smart way to speed up convergence. See section Notes in k_init for more details.
'random': choose n_clusters observations (rows) at random from data for the initial centroids.
If an ndarray is passed, it should be of shape (n_clusters, n_features) and gives the initial centers.
If a callable is passed, it should take arguments X, n_clusters and a random state and return an initialization.