K-means clustering is a popular unsupervised machine learning algorithm used for partitioning a dataset into a set of distinct, non-overlapping subgroups called clusters.
These clusters are defined in such a way that data points within the same cluster are more similar to each other than they are to data points in other clusters.
K-means is a centroid-based clustering algorithm, and it aims to minimize the variance within each cluster.
The algorithm starts by selecting K initial cluster centroids.
Data points are assigned to the nearest centroid, and centroids are updated by computing the mean of assigned data points.
This process iterates until convergence. K-means is widely used for tasks like customer segmentation and image compression. It’s efficient for large datasets but sensitive to initial centroid selection.
It may not work well with non-spherical or irregularly shaped clusters, and choosing the right K value can be challenging, often requiring domain expertise or techniques like the elbow method.
The final output of the K-means algorithm is a set of cluster assignments for each data point and the centroids of the clusters.