0
$\begingroup$

I have a dictionary containing people and the distance between each pair in the following format:

{
    "ID_person1": {"ID_person2": 100, "ID_person3": 50},
    "ID_person2": {"ID_person1": 100, "ID_person3": 40},
    "ID_person3": {"ID_person1": 50, "ID_person2": 40},
}

Since I have all distances between pairs, I'm using K-means to divide them into k clusters (groups), given k, where all elements in a group are as close as possible to the other elements in the same group.

Since K-means is iterative, is there any ohter way to detect crowds with better performance.

$\endgroup$
4
  • $\begingroup$ can you share a sample (reproducible) of the structure of your data? $\endgroup$
    – Multivac
    Jan 4, 2021 at 23:40
  • $\begingroup$ without more context, I would go with a density-based algorithm such as DBSCAN, since I assume you do not have a prior knowledge of the number of clusters $\endgroup$
    – Multivac
    Jan 4, 2021 at 23:42
  • $\begingroup$ I also assume you should create an X matrix with features for each personid for example mean distance of its k closest neighbours, closest id, remotest id, etc and whit that matrix you can now apply a cluster algorithm $\endgroup$
    – Multivac
    Jan 4, 2021 at 23:46
  • $\begingroup$ Let me edit the structure of my data. The number of clusters should be relation between the number of people and the number of elements in a group. I think it's not part of the algorithm discussed here because it's quite easy. $\endgroup$
    – Maf
    Jan 5, 2021 at 0:14

0

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service and acknowledge you have read our privacy policy.

Browse other questions tagged or ask your own question.