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  • What is k-means in Dataframe?

    K-means clustering is a technique in which we place each observation in a dataset into one of K clusters.
    The end goal is to have K clusters in which the observations within each cluster are quite similar to each other while the observations in different clusters are quite different from each other.

  • What is k-means in Python?

    K-means is an unsupervised learning method for clustering data points.
    The algorithm iteratively divides data points into K clusters by minimizing the variance in each cluster.

  • What is the k-means algorithm used for?

    K-Means clustering is an unsupervised learning algorithm.
    There is no labeled data for this clustering, unlike in supervised learning.
    K-Means performs the division of objects into clusters that share similarities and are dissimilar to the objects belonging to another cluster.

  • Each point in the dataset is assigned to the cluster of whichever centroid it's closest to.
    The "k" in "k-means" is how many centroids (that is, clusters) it creates.
    You define the k yourself.
    You could imagine each centroid capturing points through a sequence of radiating circles.

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