K-Means Clustering Algorithm is an unsupervised, iterative learning algorithm that aims to partition a dataset into 'K' pre-defined distinct non-overlapping subgroups (clusters) where each data point belongs to only one group. It is used to solve the clustering problems in machine learning or data science and is able to discover the categories of groups in unlabelled datasets on its own without the need for any training (Dabbura 2018).
Dabbura, Imad. 2018. “K-Means Clustering: Algorithm, Applications, Evaluation Methods, and Drawbacks.” Towards Data Science. 2018. https://towardsdatascience.com/k-means-clustering-algorithm-application….