TLDR
K-means clustering is a technique widely used to find groups of observations (called clusters) that share similar characteristics. This process is not driven by a specific purpose, which means you don’t have to specifically tell your algorithm how to group those observations. The algorithm uses an iterative refinement method to produce its final clustering based on the number of clusters defined by the user (represented by the variable K) and the dataset. The result is that observations (or data points) in the same group are more similar between them than other observations in another group.via the TL;DR App
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