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Which step comes first in the k-means clustering process?

Re-cluster the data points

Update centroid to take cluster mean

Choose k random observations to calculate each cluster's mean

In the k-means clustering process, the initial phase involves selecting k random observations from the dataset to serve as the initial centroids for the clusters. This selection is crucial because the initial placement of centroids can significantly influence the outcome of the clustering process. By choosing these random observations, each centroid acts as a representative for one of the clusters that will be formed.

Once the initial centroids are established, the process can move on to calculating the distance of each data point to these centroids, reassigning data points to the nearest centroid, and iteratively updating the centroids based on the means of the assigned data points. Thus, the correct understanding of this initial step sets the foundation for the subsequent calculations and iterations necessary for effective clustering.

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Calculate data point distance to centroids

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