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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?

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I worked on K-means clustering,

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.

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  • $\begingroup$ But how does one identify the clusters? Unless one limits the distances somehow? $\endgroup$ – mavavilj Jun 23 '18 at 11:51
  • $\begingroup$ 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. $\endgroup$ – mavavilj Jun 23 '18 at 11:55
  • $\begingroup$ The way I coded : for each new entry i was changing the distance/threshold, and in the end depending upon the number of dataset, you will get optimal distance from each bucket/cluster. $\endgroup$ – ratnesh Jun 23 '18 at 12:07

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