K-Means Clustering is a popular machine learning algorithm used for partitioning a dataset into K distinct, non-overlapping subgroups or clusters. It works by minimizing the variance within each cluster, thereby grouping similar data points together. This technique is particularly useful in identifying patterns and structures in complex datasets, which is essential in the field of nanotechnology.
