How KMeans algorithm works.#shorts #machinelearning #datascience #kmeans

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.

#machinelearning #dataanalysis #datascience