K-means clustering is a popular unsupervised machine learning algorithm used to group similar data points into clusters. It is a partitioning approach where the data points are divided into K non-overlapping clusters based on their similarity. The goal of K-means is to minimize the sum of squared distances within each cluster.
The process of K-means clustering can be broken down into the following steps:
Choose the number of clusters, K: The first step in K-means clustering is to determine the number of clusters to create. This can be done using domain knowledge or by trying different values of K and evaluating the quality of the clustering using metrics such as the within-cluster sum of squares (WSS).
Initialize K cluster centroids: The next step is to randomly initialize K cluster centroids, which serve as the starting points for the clustering algorithm.
Assign each data point to the nearest centroid: In this step, each data point is assigned to the nearest centroid based on the distance metric used (typically Euclidean distance).
Recalculate the centroids: After the data points are assigned to clusters, the centroid of each cluster is recalculated by taking the mean of all the data points in that cluster.
Repeat steps 3 and 4 until convergence: The previous two steps are repeated until convergence, which occurs when the assignment of data points to clusters no longer changes or a maximum number of iterations is reached.
Evaluate the clustering: Once the algorithm has converged, the quality of the clustering can be evaluated using metrics such as the WSS or silhouette score.
K-means clustering has several advantages, such as its simplicity and scalability, and can be applied to a wide range of applications, such as customer segmentation, image clustering, and anomaly detection. However, it also has some limitations, such as its sensitivity to the initial random centroid selection and its inability to handle non-linearly separable data.
To mitigate some of these limitations, extensions to the K-means algorithm have been proposed, such as fuzzy K-means, which allows for data points to belong to multiple clusters, and hierarchical K-means, which creates a hierarchy of nested clusters.