K-means clustering is utilized for what purpose?

K-means clustering is primarily utilized for partitioning data into clusters. This technique identifies groups within a dataset by grouping data points based on their similarities. It works by assigning each data point to the nearest cluster center (centroid) and iteratively adjusting the positions of the centroids based on the average positions of the points in each cluster until the clusters stabilize. The aim is to minimize the within-cluster variance, making data points in the same cluster more similar to each other compared to those in different clusters.

The other options do not accurately describe the function of K-means clustering. Sorting data alphabetically pertains to organizing data in a specific order rather than grouping similar data points. Creating labeled datasets involves a supervised learning process where data is categorized with labels, which is not the objective of K-means clustering as it is an unsupervised learning method. Analyzing time series data typically requires approaches that consider temporal dependencies, which K-means does not address since it focuses on spatial relationships among data points without regard for time.