K Means Clustering using L1 Distance Euclidean Distance Machine Learning by Dr. Mahesh Huddar

[@surajsuryawanshi7835]

Best channel for data mining and machine learning lectures.

[@MaheshHuddar]

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[@TrueTalenta]

Your videos are amazing! Please add one for DBSCAN. Thank you for your amazing work sir! You are simply a GURU in this field!

[@MaheshHuddar]

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[@lantran5419]

thanks for these ML videos, it helps a lot. Have a good day

[@MaheshHuddar]

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[@neerajyadav5375]

please Sir mentioned the lecture number

[@vmfinance619]

Good information, Sir Thank you!
How about Cost for each cluster? i..e how do we calculate K-mean objective?
would it be sum of all L1 distances for points in each cluster?

[@lakeshkumar917]

sir if we get same distance for both centers what to do then?

[@dataanalyst1012]

In k means clustering, is there an assumption in numbers of observations and variables? Would having variables greater than observation affect the results of clustering and make it less accurate?

[@chaitraparameswarappa7279]

Thank you so much Sir

[@MaheshHuddar]

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[@vmfinance619]

can u explain with an example

[@xxoeae2625]

thankyou!!

[@MaheshHuddar]

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[@saarangansrinivasan4686]

Seeing this at sem exams💀

[@MaheshHuddar]

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