Professor and Chair of Computational Methods in Architecture at Cardiff University / Prifysgol Caerdydd
Comparing the two clustering methods in #topologicpy : K_Means and DBSCAN. For this dataset, DBSCAN wins hands down and uncovers the three spirals while K_Means mixes points from different spirals., but it all depends on your data and the method parameters.
M. Kyan B.
5mo
A Very interesting visualization using #topilogicpy. The reason is that K-Means is biased toward globular-shaped clusters, whereas DBScan, Hierarchical clustering with single-link, and some other methods, are capable of producing clusters of arbitrary shapes. Thanks a lot for sharing.
Trans-Disciplinary Design Educator l Academic Researcher l Early-Stage Startup Mentor
5moIt's fascinating to see the comparison between K_Means and DBSCAN in #topologicpy. Your post reminds us that there needs to be more than one-size-fits-all solution in clustering methods. Understanding the nuances of our data and method parameters is key.