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Very good video! Just a recommendation: It would be better to specify that this is the Agglomerative Clustering, which is a specific type of hierarchical clustering, in which one starts with all points being in their own cluster. There is also Divisive Clustering, in which we iteratively divide already existing clusters, starting from a single cluster that contains all points. Great video!
Thanks for the wonderful video, Hannah! This is great material. I am preparing for a risk certification, and this really helped me revise my concepts in a much better way. Have a great day!
The Euclidean distance horizontal component at 2:17 should be 3 not 4 since 4 - 1 = 3. Also, the manhattan distance should be 4 and the maximum distance should be 3 for the same reason.
Pls endeavour to avoid making mistakes thanks for comment section i could have got it so difficult to comprehend. That aspect of sqrt of 17 is terrible. But u did well and this video is good too
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Hi many thanks for your question, Hierarchical Cluster Analysis (HCA) is not always associated with the Euclidean distance. While Euclidean distance is commonly used, HCA can work with various distance metrics depending on the nature of the data and the analysis goals. Here are some common distance metrics used in HCA: - Euclidean Distance: This is the straight-line distance between two points in a multi-dimensional space. It's one of the simplest and most widely used distance metrics. - Manhattan Distance (also known as City Block or L1 distance): This is the sum of absolute differences between coordinates. It can be suitable when diagonal movement isn't meaningful. - Cosine Similarity: This measures the cosine of the angle between two vectors, commonly used in text analysis and other contexts where vector magnitude might vary. - Mahalanobis Distance: It accounts for correlations in data by incorporating the covariance matrix, making it suitable for data with different scales and correlations among variables. - Minkowski Distance: A generalization of Euclidean and Manhattan distances, with a parameter 'p' to control the degree of the norm. - Correlation-based Distance: This distance uses the correlation between data points rather than absolute differences. It's common in gene expression analysis or other contexts where relationships between variables matter more than absolute values. I hope this was helpful : ) Regards Hannah
You just made my evening with your simple explanation.
Glad it was helpful and many thanks for your feedback! Regards Hannah
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I loved learning about "Heyrakikal" clustering
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this is an extremely good material. top-notch. never seen something so easily explained as done on this content.
Excuse me, I could use some help; would you mind? I’ve got USDT TRX20 stored in my OKX wallet with the recovery phrase (clean party soccer advance audit clean evil finish tonight involve whip action). How do I send it to BitGet?
Very good video! Just a recommendation: It would be better to specify that this is the Agglomerative Clustering, which is a specific type of hierarchical clustering, in which one starts with all points being in their own cluster. There is also Divisive Clustering, in which we iteratively divide already existing clusters, starting from a single cluster that contains all points.
Great video!
Thanks for the wonderful video, Hannah! This is great material. I am preparing for a risk certification, and this really helped me revise my concepts in a much better way. Have a great day!
I found it very understandable and simple. thanks a lot!
The Euclidean distance horizontal component at 2:17 should be 3 not 4 since 4 - 1 = 3. Also, the manhattan distance should be 4 and the maximum distance should be 3 for the same reason.
I agree they are wrong, but shouldn't it be square root of 17, which is 4.12?
Because you go 3 steps to the right and 1 up; so sqrt(3^2 + 1^2)@@playbros332
Awesome explanationa. A very wel made video. Simple and precise
Well-explained. Thanks!
I love your channel. Your explainations are so good and so clear.
Thank you so much!
thank you so much, you have explained it so well
Glad it was helpful!
Pls endeavour to avoid making mistakes thanks for comment section i could have got it so difficult to comprehend. That aspect of sqrt of 17 is terrible. But u did well and this video is good too
Hi thanks for youre feedback! We try to avoid mistakes, sorry for that and for the resulting trouble! Regards, Hannah
well, that's because it's the sqrt of 10 not sqrt of 17. The mistake was using 4 instead of 3
wonderful visuall explanatinon
Outstanding!❤
Thank you! 😊
Beautifully explained, thanks! 🙏 Incredibly clear.
Great video, thank you!!!
My pleasure!
such a very good vidio. Thank u so much for your explanation
Great video, thanks ❤
awesome thank u so much!
Thank You Madam.
How might be the sqr of 17 (16+1) =equal to 3,162 ? it must be 4,123 is not?
The error is that the x distance is 3 (from 1 to 4) not 4, so it’s the sq root of 10.
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You guys saved me
Thanks : )
well explained thank you so much
hi. it should 3 - 1 for euclidean as the formula is square of XB1-XA1
nicely explained
thank you so much. you clarified a lot!!!!
😀
How do you name the clusters? Just from left to right, so cluster 1, cluster 2, cluster 3. Or are there more methods to name a cluster?
Great content. I'm a fan :)
Glad it was helpful and many thanks for your nice feedback! Regards Hannah
youre kinda cute
I would like to RUclips tutorials like this. Do you have recommendations on what softwares to use?
DATAtab : )
@DATAtab where can you learn more about it?
I would like to ask, is Hierarchical Cluster Analysis always associated with the Euclidean Distance? Thank you
Hi many thanks for your question, Hierarchical Cluster Analysis (HCA) is not always associated with the Euclidean distance. While Euclidean distance is commonly used, HCA can work with various distance metrics depending on the nature of the data and the analysis goals.
Here are some common distance metrics used in HCA:
- Euclidean Distance: This is the straight-line distance between two points in a multi-dimensional space. It's one of the simplest and most widely used distance metrics.
- Manhattan Distance (also known as City Block or L1 distance): This is the sum of absolute differences between coordinates. It can be suitable when diagonal movement isn't meaningful.
- Cosine Similarity: This measures the cosine of the angle between two vectors, commonly used in text analysis and other contexts where vector magnitude might vary.
- Mahalanobis Distance: It accounts for correlations in data by incorporating the covariance matrix, making it suitable for data with different scales and correlations among variables.
- Minkowski Distance: A generalization of Euclidean and Manhattan distances, with a parameter 'p' to control the degree of the norm.
- Correlation-based Distance: This distance uses the correlation between data points rather than absolute differences. It's common in gene expression analysis or other contexts where relationships between variables matter more than absolute values.
I hope this was helpful : ) Regards Hannah
Is this Agglomerative Clustering?
How you calculate the distances between Lisa, Joe with the others?? you have a group of positions not just one... how do you do that? thankss!
Hi, in this case you would first calcualte the center between Lisa and Joe and then the diestance from this center to one other Person. Regards Hannah
Real good!
i love your accent
: )
hi where can i find the elbo method
Oh sorry, it will be there soon!!!
Nice video!
I want to know the name of algorithm that you have used here to explain hierarchical clustering.
I want too, but it is hight probable that she does not tell us. Statistics saying.
@@Nothingimportant1 AGNES
4-1=3 though!
: )
خدا خیرت بده بانو
is and not und at 3:15
Thanks : )
Excellent explanation. Why it takes too long to create a new video?
Good question! : ) We need almost two weeks to prepare the topic and to create the slides! Regards Hannah
@@datatab i hope it will be fast :)
klaaaaaaster
klaaastarrrrss
Claaaastars 😂
i think you have a mistakes with calculating