It's the common mistakes that we do or we can say we got stuck with the point that root and square get's cancel. Actually in mathematics it's not like that , whenever there is square inside a root then if you want to remove both the operator then you have to leave a modulus outside. On the other hand if there is a root inside the square then we don't need modulus. For example:- √(x²) = |x| (√x)² = x Hope you get it.
Reallocation of cluster unless it matches the last findings. This means you have found the best partitioning and hence new calculation will not make a change.
@@MaheshHuddar won't the final clusters be different based on different initial centroids we choose if we are not given any initial centroid in question?
thank you sirrr ,nice teaching
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If both the distance to m1 and m2 are same which cluster do we need to allocate ? To new cluster ?
Assign to any one cluster and continue with new iteration
Small clarification : Square and root gets cancel in mathematics .. so the formulea is d(x2,x1) = x2-x1 -- Isn't it ?
if you cancel it or not but the answer remains the same right
The points are one dimensional here. But for higher dimension points we need to sum all squares of all differences under root.
To avoid minus sign
Square is compulsory or take abs val
It's the common mistakes that we do or we can say we got stuck with the point that root and square get's cancel. Actually in mathematics it's not like that , whenever there is square inside a root then if you want to remove both the operator then you have to leave a modulus outside. On the other hand if there is a root inside the square then we don't need modulus.
For example:-
√(x²) = |x|
(√x)² = x
Hope you get it.
Ofcourse, it would be just like measuring distances using scale since its 1-D
Sir what if i get same distances
And to which cluster do i need to assign it
Randomly assigned to one cluster and continue with new iteration
Why we repeat the process of new clusters for 4 time ? is there any specific reason ? can we do it just for one time ?
Reallocation of cluster unless it matches the last findings. This means you have found the best partitioning and hence new calculation will not make a change.
Sema explain sir I have clearly understand TQ sir
Thanks and welcome
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Thanks 💫
Can we use this method to create 3 clusters?? Pls reply
yes, you need an extra centroid value and then do the calculation as told in the video.
Thank you sir..
Most welcome
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thanks bro
thnx
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If initial centroids are not given what should l do
apni choice kai according choose kar loa
You can select any data points as initial centroids
@@MaheshHuddar won't the final clusters be different based on different initial centroids we choose if we are not given any initial centroid in question?
@@sadiyaww7507 No,
You can start with any centroids randomly, if not given.
Algorithm converges to correct clusters finally
Leave the exam hall and go back to home 🏡
❤
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