k-adapted means clustering algorithm

yes it can. k means algorithm for grouping particles and its used for multimodal function optimization

K-Nearest Neighbors is a supervised classification algorithm, while k-means clustering is an unsupervised clustering algorithm. While the mechanisms may seem similar at first, what this really means is that in order for K-Nearest Neighbors to work, you need labeled data you want to classify an unlabeled point into (thus the nearest neighbor part). K-means clustering requires only a set of unlabeled points and a threshold: the algorithm will take unlabeled points and gradually learn how to cluster them into groups by computing the mean of the distance between different points.

K-means clustering is a data mining learning algorithm used to cluster observations into groups of related observation without any prior knowledge of those relationships.

Algorithm means written expression of any data.