Numerical Machine Learning

K-means Clustering

Author(s): Zhiyuan Wang*, Sayed Ameenuddin Irfan*, Christopher Teoh* and Priyanka Hriday Bhoyar *

Pp: 194-211 (18)

DOI: 10.2174/9789815136982123010009

* (Excluding Mailing and Handling)


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.

Keywords: K-Means Clustering, Distance Metrics, Numerical Example, Small Dataset, Scikit-Learn

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