Solved Example Complete Linkage - Agglomerative Hierarchical Clustering Euclidean Dist Mahesh Huddar

[@thomaswitney365]

I appreciate the very simple and easy explanation, thank you!

[@einsteinmunachi]

This is absolutely lovely, thank you so much for this explanation.... 🙏

[@nevilmehta2281]

Really good explanation! Thanks a lot for such an in-depth explanation

[@MaheshHuddar]

Welcome Do like share and subscribe

[@LauraMortimore-r7d]

Thank-you so much!! You are a life saver!

[@ganeshjaggineni4097]

NICE SUPER EXCELLENT MOTIVATED

[@MaheshHuddar]

Thank You Do like share and subscribe

[@SumanKumari-cn3wo]

Precise explanation..thank you sir

[@MaheshHuddar]

Welcome Do like share and subscribe

[@zainellahi3380]

hi, what if the distance was manhattan distance? So to calculate would it be (5-4) which is 1 which is the same as the euclidean distance

[@gkfactsandquiz3056]

Yes but in case of points like (1,2) and (3,1) it should be done in Euclids distance ,so for this reason he explained in such a way😊

[@sahithimudumbai8964]

Do they mention in the question saying it should be solved using complete linkage or single linkage?

[@MaheshHuddar]

Yes

[@Kath0di0n]

good video

[@MaheshHuddar]

Thanks Do like share and subscribe

[@sanidas7922]

but for complete linkage we have to take the maximum distance?

[@MaheshHuddar]

Follow this video kzbin.info/www/bejne/ZnKTqKeqpJ6qfrc

[@sanidas7922]

@@MaheshHuddar ok sir...got it...thank you so much

[@abhinavbaranwal5377]

Can you share the slides also ?

[@einsteinmunachi]

Assuming it was average linkage for a dataset {2,3,7}... will the average pairwise distance between 2,3 be: Square_root((2,3)^2) / 2 = 0.5