Welcome! If you are like me you probably have a passion for Lineage 2.
Lineage 2 fraps collection from retail servers. Old frapses & new frapses uploaded as soon as they are released. Legacy videos for the nostalgic people. Good times should never be forgotten.
Also check my playlist Lineage 2 retail - for a list of fraps uploaded by others.
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Legends. Jasinn, my love. 11y gone
the best time of l2
wow, i was yrkoon (cardinal) maybe some ppl remember me :P
((( Какая же ностальгия! Классика не умрет, она в наших сердцах
Kak davno eto 6blJlo
Amazing video David, keep teaching bro!!
I understand the explanation but using the X1 and X2 to indicate the cluster is quite confusing. Otherwise it is well done!
Is this the same procedure with euclidean distance excluding the formula? Do I have to consider some additional stuff while doing this with euclidean?
lol this dude didn't care at all haha :D cool ! Stupid EE picks up zariche, kinda worthless ?
"ciubuca: ivalu u lost aq and drac set ?" The moment i quit l2 because of monkii the scammer albanian boy living in sweden Grr
Adunaphel 🤩
Thank you for this , it was really helpful
sad
bad way of explaining things
Good old days
Thank you. really nice explanation for me.
серв лохов :D и ДА лох)
Why we calculate central points ?
worse explain
Omg you never use arcane power...lolsps learn how ti play mage
Worse explanation
what is this? a tutorial without indian accent?
Teaching skill is a talent.
Thanks, i have read a lot of theory but this is perfect.
what is you have minus values? for example -1 and 1 which one should I select?
These videos are great - miss those days. I miss Lineage 2 :( Erica and Franz servers...
how do you recalculate the cluster means?
For those who do not understand the tables and how the distance was acquired : Distance is measured by taking coordinates of for example a and c, so the distance is measured as Sqrt((a1-b1)SQUARED+(a2-b2)SQUARED)
Thank you :)
very helpful thanks
best explanation I would say
thank u for the much simplified explaining and thank u for the British accent ^^
This could have better if you showed the calculation of the euclidean distance between i and the centroids. Also, although you stated that in the end of the video that the next iteration will not result in a new centroid, you should have shown this.
ありがとうございます”!!from japan!
you really saved me for my exam next day thanks!!
2:50 adris runs to die behind a rock, dem plays.
How many times daimos got res on that anthy pewpew ? xD good times <3 franz
thenks
Great video. thank you
Thanks a lot, very helpful
Thank you. So helpful video.
Thank you very much for illustrating how to do clustering using k-means and Euclidean distance. It helped me a lot in my homework.
very bad explanation. You should explain in the way that those who does not know about that understand it.But it seems you explain it to people who know what is the concept.
Its Fun to see myself 10 years later XD
how Conversion from unsupervised to supuervised?
use k nearest neighbors algorithm rather than using k-means!
can you do a video on PAM? this video was great
how is the distance calculated? and how did values for X1's 3rd row calculated?
Distance calculated = Sqrt((x1-x2)^2 + (y1-y2)^2)
Euclidean Distance
Good illustration but could not understand how you have calculated Means of points!
To calculate means from cluster centers: For example, if a cluster contains three data points such as {32,65}, {16,87} and {17,60}, the mean of this cluster is (32+16+17)/3 and (65+87+60)/3.
Thanks! you just saved my 15 marks! :)
thanks for the example! simple and clear.
There are co-ordinates for points in your example. What if we want to apply K - Means Clustering for image segmentation of gray scale image. Can we do it by only taking Intensity Values( Gray Values) of pixel? Please Help . Very Well explained ... Thanks in advance .