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  1. Jun 26, 2024The k-means algorithm is a widely used method in cluster analysis because it is efficient, effective and simple. K-means is an iterative, centroid-based clustering algorithm that partitions a dataset into similar groups based on the distance between their centroids. The centroid, or cluster center, is either the mean or median of all the points ...
  2. towardsdatascience.com

    K-means is an unsupervised clustering algorithm designed to partition unlabelled data into a certain number (thats the " K") of distinct groupings.In other words, k-means finds observations that share important characteristics and classifies them together into clusters.
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  1. K-means++

    In data mining, k-means++ is an algorithm for choosing the initial values for the k-means clustering algorithm. It was proposed in 2007 by David Arthur and Sergei Vassilvitskii, as an approximation algorithm for the NP-hard k-means problem—a way of avoiding the sometimes poor clusterings found by the standard k-means algorithm. It is similar to the first of three seeding methods proposed, in independent work, in 2006 by Rafail Ostrovsky, Yuval Rabani, Leonard Schulman and Chaitanya Swamy. Wikipedia

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