Briefly describe the Iterative K-means clustering algorithm? 

 The Iterative K-means clustering algorithm is a type of unsupervised learning algorithm that is used for clustering and pattern recognition. It is a simple and efficient algorithm that can be used to partition data into groups or clusters, where each group or cluster represents a set of similar objects. The algorithm is based on the K-means clustering method and is used to iteratively refine the results of the K-means algorithm.

The basic steps of the Iterative K-means clustering algorithm are as follows:

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