How KMeans algorithm works.
K-means clustering is a method of clustering, or grouping, data into clusters based on similarity. It is an iterative algorithm that assigns data points to a specific number of clusters (K) by minimizing the sum of squared distances between the data points and the centroid (mean) of the cluster.
The algorithm starts by randomly selecting K initial centroids and assigning each data point to the closest centroid, forming K clusters. The centroids are then updated to the mean of the data points in the cluster, and the process is repeated until the centroids do not change or a maximum number of iterations is reached.
One of the main advantages of k-means clustering is its simplicity and speed, making it a popular choice for clustering large datasets. However, it can be sensitive to the initial selection of centroids and may not always produce the best clustering solution.
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