K Means Clustering algorithm #machinelearning #ai #cluster #unsupervisedlearning #kmeansclustering

K-means clustering is a popular unsupervised machine learning algorithm used for partitioning a dataset into K distinct, non-overlapping clusters. The goal is to group data points into clusters such that points in the same cluster are similar to each other while being dissimilar to points in other clusters. Here's how it works: 1.Initialization: Choose K initial centroids randomly from the data points. These centroids represent the centers of the clusters. 2.Assignment: Assign each data point to the nearest centroid, forming K clusters. 3.Update: Recalculate the centroids of the clusters by taking the mean of all data points assigned to each centroid. 4.Repeat: Repeat steps 2 and 3 until the centroids no longer change significantly, or until a specified number of iterations is reached. Overall, K-means clustering is a versatile tool for exploratory data analysis, pattern recognition, and clustering applications in various fields, including image segmentation, document clustering, customer segmentation, and anomaly detection.