Beginning today, we are initiating a series of articles on data clustering, featuring a comparison of significant algorithms in the field (in particular K-Means and DBSCAN). Our goal is to blend theoretical concepts with practical implementation (in C#), offering clear illustrations of the associated challenges.
Read more

In this post, we offer a concise overview of clustering, delve into the accompanying challenges, and introduce various state-of-the-art algorithms.
Read more

Before delving into the C# implementation of the three algorithms, we will briefly outline the logical structure of the Visual Studio project in this post.
Read more

In this post, we will witness the most widely used clustering algorithm in action and assess its sensitivity to outliers and irregular shapes.
Read more

In this post, we continue our exploration by examining the characteristics of hierarchical clustering.
Read more

In this concluding post, we will explore the DBSCAN algorithm and demonstrate how it can be employed to alleviate the limitations of both K-Means and hierarchical clustering.
Read more