Abstract
In this chapter, we explore the K-means clustering algorithm, emphasizing an
accessible approach by minimizing abstract mathematical theories. We present a concrete
numerical example with a small dataset to illustrate how clusters can be formed using the Kmeans clustering algorithm. Additionally, we provide sample codes and comparisons with the
K-means model available in the scikit-learn library. Upon completing this chapter, readers will
gain a comprehensive understanding of the mechanics behind K-means clustering, and its
connection to the implementation and performance of the algorithm, and be well-prepared to
apply it in practical use.
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