DBSCAN Clustering Algorithm Solved Numerical Example in Machine Learning Data Mining Mahesh Huddar
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- Опубликовано: 4 окт 2024
- DBSCAN Clustering Algorithm Solved Numerical Example in Machine Learning Data Mining by Mahesh Huddar
DBSCAN
Density-based spatial clustering of applications with noise is a data clustering algorithm
Data Points:
P1: (3, 7) P2: (4, 6)
P3: (5, 5) P4: (6, 4)
P5: (7, 3) P6: (6, 2)
P7: (7, 2) P8: (8, 4)
P9: (3, 3) P10: (2, 6)
P11: (3, 5) P12: (2, 4)
Apply the DBSCAN algorithm to the given data points and Create the clusters with minPts = 4 and epsilon (ε) = 1.9.
The following concepts are discussed:
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Perfect one! Without any theory explanation. Majority of us (i mean people who are looking for such an example) are familiar with theory but it is hard to find direct implementetion of it.
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usually I don't comment even on good channels but your way of delivering the content is really good, the way I like: No repetition of words, maintaining a steady flow, no use of touch words, no use of filler words.(usually other RUclipsrs do this trick to increase the length of the video for engaging the audience for longer time which build a habit of coming back to the channel for more content).
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tomrw is my exam thank you sirr well explained my professor took hours to explain this 😄😄
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Nice slide and explanation. You just easily overview the topic within 11 minutes. Thanks ❤
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Great video with a nice example, detailed calculation and clear explanation!
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Thank you very much!!! It is very clear and concise explanation
You are welcome!
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Thank you very much, it's really interesting and understood easily for your exolanation about DBScan with the real case
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sir could you please explain that......... how would you plot the graph points
from 0:10 ro 0:40 the points shown numerically here are just plotted on graph
@@skarthikeya5285 u cleared my doubt TQ
Tq
@@skarthikeya5285Tq
Great Teaching.
Thank you
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This explanation is very good. I have seen other videos too. Just one suggestion please dont use "particular" in every line that to twice or thrice. It sometime breaks the flow of listening and too irritating.
Ok
You are blowing my brain ❤❤❤ amazing explanation
Thank you so much
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very good explanation and slides, Thanks
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amazing channel with exceptional explanation. always look for your videos when im searching a topic on youtube
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Thank you so much sir❤
Most welcome
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You are the man bro, amazing job
Glad it helped
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❤
Excellent explanation sir !!
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Well explained 👍
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Thank you a lot.
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If the minpts and e is not given what can we do sir. can we determine with heuristic method
Thank you so much!
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Excellent explanation, thank you so much!
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Super explanation sir
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But here point 2 and 11 belongs to two different clusters? how can we explain that?
Thank you sir
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sir in another video u say that we have to consider value greater than threashlod value but in this video u are saying that we have to consider value less than threashold could u please give me clarity about this ASAP
In first example we calculated the distance, there we need to select the minimum distance
In second example we have used similarity Matrix, hence we need to use highest similarity value
ammamaa bhagwan ho mero lagi yo sir :)
can the clusters overlap ?
Sir ye slides aapkp kha sey milee, cause ye same slided hamari ma'am bhe copy ki hain. can you please tell me
Great explanation sir
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excellent lesson, i will have an exam in a few days where I cannot code, where can I find similar practice questions?
Numerical examples..?
@@MaheshHuddar yes, both supervised and unsupervised, not in python
Sir can we use Manhattan distance formula ?
Yes
You can use any distance metric
@@MaheshHuddar Thank You Sir For Your Quick Response !!
Can we use manhattan formula insted of euclidean
Yes you can use any distance metric
How to form clusters for points with more than two coordinates?
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amazing sir🤩
Thanks a lot 😊
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thank you
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How the graph is pointed can anyone explain
First all given points (x-axis,y-axis) in the question were plotted on graph. Then clustered according to the core points
👏👏👏
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may I have your file ppt of this video?
CLARANS
🙏🏼🫡
Thank you so much sir❤🙏
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Thank you so much!
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