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The Iterative K-Means Clustering Algorithm is a popular unsupervised machine learning technique used for partitioning a dataset into K distinct, non-overlapping clusters based on feature similarity. It is widely used in applications such as pattern recognition, market segmentation, image compression, and anomaly detection. The algorithm aims to minimize the variance within each cluster while maximizing the variance between clusters. It is called “iterative” because it repeatedly adjusts the centroids and cluster assignments to optimize results. ### Fundamental Concepts The K-Means algorithm operates by organizing a set of n data points into K clusters, where each data point belongs to the cluster with the nearest mean (centroid). The term ‘means’ refers to averaging of the data points in a cluster to find the centroid. The algorithm assumes that the number of clusters (K) is predefined by the user. ### Main Steps of Iterative K-Means Clustering Algorithm The algorithm follows these basic steps: 1. Initialization: * Select K initial centroids randomly from the dataset. These centroids can be randomly chosen data points or generated through heuristics such as the K-Means++ method. * These centroids represent the initial guess of the cluster centers. 2. Assignment Step: * Each data point is assigned to the nearest centroid using a distance metric, typically Euclidean ________ __________ _____ _________ ________ ___ ___ _________.
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