This article provides an overview of core data science algorithms used in statistical data analysis, specifically k-means and k-medoids clustering.

Clustering is one of the major techniques used for statistical data analysis.

As the term suggests, “clustering” is defined as the process of gathering similar objects into different groups or distribution of datasets into subsets with a defined distance measure.

K-means clustering is touted as a foundational algorithm every data scientist ought to have in their toolbox. The popularity of the algorithm in the data science industry is due to its extraordinary features:

• Simplicity
• Speed
• Efficiency

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