K-means clustering is a popular and widely used unsupervised machine learning algorithm that is employed to group data points into clusters based on their similarity. The goal of K-means clustering is to partition a dataset into K clusters, with K being a user-defined parameter.
The algorithm operates by iteratively assigning data points to clusters in such a way that the variance within each cluster is minimized. It does this through the following steps:
- Initialization: K initial cluster centroids are randomly selected from the dataset. These centroids act as the centers of the clusters.
- Assignment: Each data point is assigned to the cluster whose centroid is closest to it. Typically, the Euclidean distance is used as a measure of similarity, but other distance metrics can also be employed.
- Update: The centroids of the clusters are recalculated as the mean of all data points assigned to each cluster.
- Re-assignment: Steps 2 and 3 are repeated iteratively until the assignment of data points to clusters no longer changes significantly or a specified number of iterations is reached.
K-means is effective when the data clusters are spherical or roughly spherical and have a similar size. It is widely used for tasks such as customer segmentation, image compression, and document classification. However, it has limitations, including sensitivity to the initial placement of centroids, the need to specify the number of clusters (K) in advance, and vulnerability to outliers.
Despite its limitations, K-means clustering remains a valuable tool for data analysis and pattern recognition, and it is relatively efficient and straightforward to implement. Researchers and analysts often use K-means as a starting point for exploring and understanding patterns within their data.