Your way of teaching is simple, clear, concise. I appreciate your effort. You may not be hearing it frequently, but people like you value a lot for people like us. Keep doing it. Thanks for sharing
Hello Dilip, Thank you for all nice words, it means a lot for me to motivate myself and make many more videos. Happy to know this Proximity Measures Tutorial Video helped to learn. Keep Learning !!
I think the professors go through such high level abstractive thinking that they don't bother going through the step by step solution to numerical problems. Videos like this help a lot in this regard.
Our university course instructor does not go though the numerical problems. They expect us to self study those. But when I read the text book, I don't find any step by step solution to the problems. Your videos have been a tremendous help. Unlike some youtubers, you seem like an ardent persona of what an ideal teacher should be. God bless you kind sir.
sir i love your teaching and i have watched whole your playlist you taught me very good more good than my iit prof could taught , keep making video sir , by watching your video I decided to watch every possible video from your channel
Hi Very clear step-by-step process. But I felt you missed the last most important point is what does the final dissimilarity matrix produced tells us? How do you interpret the final result? Can we form clusters just by observing the results or do we need to apply k-Means or hierarchical clustering technique to arrive at the answer? Appreciate if you could explain what the final table tells us in terms of the mixed data we had at the beginning. Appreciate your input.
This is very detailed explanation. It helped me put lesser effort to understand the Proximity Measures for different Attributed. Can you help me understand what the formula for measuring the proximity for Numeric attribute is different here. Previous video you taught about Euclidean, Manhattan and Supermum distance methods. Here you are using a different formula. Kindly advice on this change in formula.
Sir is the normalisation for numerical attribute is done for mixed attribute only or should do we normalise while individually solving for numeric attributes also?
Thank you so much for taking the time to watch my video on Proximity Measure and for your kind words! I'm glad to hear that you found it informative and I appreciate your feedback. Your encouragement means a lot to me and it motivates me to keep creating more content. Thanks again for your support!
Good detail video in the playlist. One question which distance formula are you using to find the distance for the numerical data in this Part-7. Where you have the numerator with the difference of the max and min of Test-3 scores and the numerator is the difference of X(if) and X(jf). What is the name of this formula?
im guessing to bring all the dissimalirity matrices onto the same scale so that the result makes more sense. he has used manhattan with normalilsation (by dividing by max-min )
very imformative. by normalizing numeric attributes do we also compute the distance? and if we have two numerical attributes do we have to normalize them seperately?
Your way of teaching is simple, clear, concise. I appreciate your effort. You may not be hearing it frequently, but people like you value a lot for people like us. Keep doing it. Thanks for sharing
Hello Dilip, Thank you for all nice words, it means a lot for me to motivate myself and make many more videos. Happy to know this Proximity Measures Tutorial Video helped to learn. Keep Learning !!
Yeah this is real teaching... Way better than professors who do not spend time to really teach in university.
I think the professors go through such high level abstractive thinking that they don't bother going through the step by step solution to numerical problems. Videos like this help a lot in this regard.
Our university course instructor does not go though the numerical problems. They expect us to self study those. But when I read the text book, I don't find any step by step solution to the problems. Your videos have been a tremendous help. Unlike some youtubers, you seem like an ardent persona of what an ideal teacher should be. God bless you kind sir.
What a clear and wonderful explanation！！You save my exam😭😭
Yes, his whole video seris on data mining is absolutely wonderful.
really useful sir thanks you for making it so easy
sir i love your teaching and i have watched whole your playlist you taught me very good more good than my iit prof could taught , keep making video sir , by watching your video I decided to watch every possible video from your channel
yr binod bhai jan bacha li apne yr . great yr keep it up
I come across some textbooks and professors that are very good at explaining simple concepts in a very complicated way
Thankyou for saving me.✌✌ I am grateful to you. This topic was very necessary for end semester exams in CSE.
Very simple way of teaching and neat and clear work on the board. Only thing is camera is shaky.
Nice explanation Binod. Thank you very much
Thank you for all kind words. Glad to hear this proximity tutorial videos helped you. Keep Learning !!
Thank u so much you are great ❤
Please teach other lessons in data mining
thankyou so much for explaining this concept in such easy terms...
Very helpful before the exam sir! ❤️
Your videos helped me a lot in my course work . keep doing it
jazak ALLAH............really helped me
Thank you sir.This video is very useful and very good explanation.keep it up sir.
🥳
Thank you Isuri !!
Thanks a lot sir, I understand it with your descriptions about formulas... Again thanks
Thank you so much for your effort! that was a very clear explanation, you made the topic much easier
Thanks a lot Sir
from Senegal
Thank you for your nice words and greet. Happy learning 🙂.
Your videos are the best! It helped me so much I appreciate your efforts thank you so much 🙏🏼🙏🏼🙏🏼
easy understanding and very helpful.....thank you so much
Wow your teaching is so good Thank you.
Thank you so much for your nice words. It means a lot for me. Keep Learning !!
your just toooo good sir, thank you!!
Excellent Series!!!!
thank you so much, it was very helpful play list.
Good to know this Proximity Measure Video series was useful. Thank you for all nice words and it motivated me. Keep Learning !!
Watched the whole series sir, awesome work! Keep up the good work, already subscribed.
thank you very much, understood the concept easily
Hi
Very clear step-by-step process. But I felt you missed the last most important point is what does the final dissimilarity matrix produced tells us? How do you interpret the final result? Can we form clusters just by observing the results or do we need to apply k-Means or hierarchical clustering technique to arrive at the answer? Appreciate if you could explain what the final table tells us in terms of the mixed data we had at the beginning. Appreciate your input.
Thanks a Lot sir, this topic is very very important and your explanation is once again crystal clear
Thanks Bhaskar to appreciate Proximity Measures Video series. Keep Learning !!
Thank you so much sir
This is very detailed explanation. It helped me put lesser effort to understand the Proximity Measures for different Attributed. Can you help me understand what the formula for measuring the proximity for Numeric attribute is different here. Previous video you taught about Euclidean, Manhattan and Supermum distance methods. Here you are using a different formula. Kindly advice on this change in formula.
Sir is the normalisation for numerical attribute is done for mixed attribute only or should do we normalise while individually solving for numeric attributes also?
You are awesome sir
Veryyyy Helpful! THankyou sir
Thank you so much for taking the time to watch my video on Proximity Measure and for your kind words! I'm glad to hear that you found it informative and I appreciate your feedback. Your encouragement means a lot to me and it motivates me to keep creating more content. Thanks again for your support!
@@binodsuman yes sir thankyou again!
Thanks sir nice explanation
Good detail video in the playlist. One question which distance formula are you using to find the distance for the numerical data in this Part-7. Where you have the numerator with the difference of the max and min of Test-3 scores and the numerator is the difference of X(if) and X(jf). What is the name of this formula?
But in your example for numeric data you have used Eucledian/manhattan without normalizing them. Then why normalization is required here ?
im guessing to bring all the dissimalirity matrices onto the same scale so that the result makes more sense. he has used manhattan with normalilsation (by dividing by max-min )
Thanks a lot sir !
very imformative. by normalizing numeric attributes do we also compute the distance? and if we have two numerical attributes do we have to normalize them seperately?
Thank you so much!
Thank you so much...
thank you sir
thanku sir...
Thanks a lot
Good to know Wira, this Proximity Measures Tutorial Series helped you. Keep Learning !!
What if there are multiple numerical columns?
i got 0.50 for d(3,2) of mixed attributes
camera man ko zyada paisa chahiye