K-Means Clustering using Wallacei

Hi @milad.showkatbakhsh and @mmakki_10 and dear community, hope you are doing well.

There are some questions about how K-means clustering is working in Wallacei. As we know it is an iterative method to find the clusters. Is it working iteratively in Wallacei by running it once? Because every time I run it, it gives me different clustering with different cluster centers. When I press run continuously, in the end, the process disappears due to an error that tells “There are similarities of the individuals and the number of clusters needs to be adjusted.”
So am I supposed to choose a cluster between those Wallacei is suggesting to me? Also, I want to know about how the process is behind the scene.


Hello Nariman,

K-means clustering always starts by randomly assigning the initial cluster centres and iteratively adjusts them throughout the internal process. So everytime you run this, clusters might be slightly different due to this randomness involved inside. it is a fast method to quickly group individuals based on their similar performances.