# What's the common way to utilize distance functions for clustering?

What's the common way to utilize distance functions for clustering?

Like does one set some thresholds for the distances and do grouping based on that?

you dont need any thresholds for clustering the data, your algorithm with club your data into appropriate buckets, for each input data , your bucket entries will change, only one thing you need to do : define $k$, i.e number of buckets , usually, $k = n^\frac{1}{2}, n=$ number of data set.
• p. 7 here: cs-people.bu.edu/evimaria/cs565/lect7.pdf. Shows that one creates the clusters by utilizing a distance function $f(X,d) = Γ$ that operates on partitions $Γ$ of the data set $X$. So in a sense, there's a "threshold". It's how the partitions are separated. – mavavilj Jun 23 '18 at 11:55