K Means Clustering Solved Example K Means Clustering Algorithm in Machine Learning by Mahesh Huddar

K Means Clustering Solved Example K Means Clustering Algorithm in Machine Learning by Mahesh Huddar
Use K Means clustering to cluster the following data into two groups. Assume cluster centroid are m1=4 and m2=11. The distance function used is Euclidean distance. { 2, 4, 10, 12, 3, 20, 30, 11, 25 }
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[@prathmeshmohite692]

thank you sirrr ,nice teaching

[@MaheshHuddar]

Welcome
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[@ravikolagani]

Small clarification : Square and root gets cancel in mathematics .. so the formulea is d(x2,x1) = x2-x1 -- Isn't it ?

[@darkzone3295]

if you cancel it or not but the answer remains the same right

[@lucaspecialedits4284]

The points are one dimensional here. But for higher dimension points we need to sum all squares of all differences under root.

[@user-ke4ob6iu2r]

To avoid minus sign
Square is compulsory or take abs val

[@sheevanulhaq3912]

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.

[@tusharsoni_42]

thnx

[@MaheshHuddar]

Welcome
Do like share and subscribe

[@rakhiakshayrakhi9835]

If initial centroids are not given what should l do

[@mrchocon5388]

apni choice kai according choose kar loa

[@MaheshHuddar]

You can select any data points as initial centroids

[@sadiyaww7507]

@@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?

[@MaheshHuddar]

@@sadiyaww7507 No,
You can start with any centroids randomly, if not given.
Algorithm converges to correct clusters finally