Why KMeans May Not Be The Apt Clustering Algorithm Always

KMeans is a popular clustering algorithm. Yet, its limitations make it inapplicable in many cases.

For instance, KMeans clusters the points purely based on locality from centroids. Thus, it can create wrong clusters when data points have arbitrary shapes.

Among the many possible alternatives is DBSCAN, which is a density-based clustering algorithm. Thus, it can identify clusters of arbitrary shape and size.

This makes it robust to data with non-spherical clusters and varying densities. Find more info in the comments.

Find more here: Sklearn Guide.

Share this post on LinkedIn: Post Link.

---

Find the code for my tips here: GitHub.

I like to explore, experiment and write about data science concepts and tools. You can read my articles on Medium. Also, you can connect with me on LinkedIn.