WebK-Means is guaranteed to converge to a local optimum. However, that does not necessarily have to be the best overall solution (global optimum). The final clustering result can depend on the selection of initial centroids, so a lot of thought has been given to this problem. WebSome examples of unsupervised machine learning algorithms include K-means clustering. When your boss give you the data which is unlabeled and you were asked to build a …

Smart initialization via k-means++ - Clustering with k-means - Coursera

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K-Means Pros & Cons HolyPython.com

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