DBSCAN Clustering Algorithm Solved Numerical Example in Machine Learning Data Mining Mahesh Huddar
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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Пікірлер: 81
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.
@MaheshHuddar
Жыл бұрын
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Great video with a nice example, detailed calculation and clear explanation!
@MaheshHuddar
7 ай бұрын
Thank You Please do like share and subscribe
Nice slide and explanation. You just easily overview the topic within 11 minutes. Thanks ❤
@MaheshHuddar
7 ай бұрын
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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 KZreadrs 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).
@MaheshHuddar
9 ай бұрын
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tomrw is my exam thank you sirr well explained my professor took hours to explain this 😄😄
@MaheshHuddar
Жыл бұрын
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Thank you very much, it's really interesting and understood easily for your exolanation about DBScan with the real case
@MaheshHuddar
8 ай бұрын
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Thank you very much!!! It is very clear and concise explanation
@MaheshHuddar
Ай бұрын
You are welcome! Do like share and subscribe
Excellent explanation, thank you so much!
@MaheshHuddar
Жыл бұрын
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amazing channel with exceptional explanation. always look for your videos when im searching a topic on youtube
@MaheshHuddar
8 ай бұрын
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very good explanation and slides, Thanks
@MaheshHuddar
7 ай бұрын
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You are the man bro, amazing job
@MaheshHuddar
3 ай бұрын
Glad it helped Do like share and subscribe
Thank you so much!
@MaheshHuddar
7 ай бұрын
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If the minpts and e is not given what can we do sir. can we determine with heuristic method
You are blowing my brain ❤❤❤ amazing explanation
@MaheshHuddar
3 ай бұрын
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Excellent explanation sir !!
@MaheshHuddar
7 ай бұрын
Thank You Do like share and subscribe
Thank you a lot.
@MaheshHuddar
3 ай бұрын
Welcome Do like share and subscribe
Thank you so much sir❤
@MaheshHuddar
6 ай бұрын
Most welcome Do like share and subscribe
Thank you sir
@MaheshHuddar
7 ай бұрын
Welcome Please do like share and subscribe
Great Teaching. Thank you
@MaheshHuddar
9 ай бұрын
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Great explanation sir
@MaheshHuddar
Жыл бұрын
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Well explained 👍
@MaheshHuddar
2 ай бұрын
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Super explanation sir
@MaheshHuddar
Жыл бұрын
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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
ammamaa bhagwan ho mero lagi yo sir :)
amazing sir🤩
@MaheshHuddar
8 ай бұрын
Thanks a lot 😊 Do like share and subscribe
👍
@MaheshHuddar
6 ай бұрын
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thank you
@MaheshHuddar
8 ай бұрын
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But here point 2 and 11 belongs to two different clusters? how can we explain that?
sir could you please explain that......... how would you plot the graph points
@skarthikeya5285
Жыл бұрын
from 0:10 ro 0:40 the points shown numerically here are just plotted on graph
@jimintata5091
8 ай бұрын
@@skarthikeya5285 u cleared my doubt TQ
@ironman5255
7 ай бұрын
Tq
@Manjith-1887
6 ай бұрын
@@skarthikeya5285Tq
excellent lesson, i will have an exam in a few days where I cannot code, where can I find similar practice questions?
@MaheshHuddar
Жыл бұрын
Numerical examples..?
@fintech1378
Жыл бұрын
@@MaheshHuddar yes, both supervised and unsupervised, not in python
Can we use manhattan formula insted of euclidean
@MaheshHuddar
Ай бұрын
Yes you can use any distance metric
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.
@MaheshHuddar
3 ай бұрын
Ok
How to form clusters for points with more than two coordinates?
How the graph is pointed can anyone explain
@donivanka
3 ай бұрын
First all given points (x-axis,y-axis) in the question were plotted on graph. Then clustered according to the core points
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
@MaheshHuddar
2 ай бұрын
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
Sir can we use Manhattan distance formula ?
@MaheshHuddar
Ай бұрын
Yes You can use any distance metric
@Nikhil.Rebelling
Ай бұрын
@@MaheshHuddar Thank You Sir For Your Quick Response !!
may I have your file ppt of this video?
CLARANS
👏👏👏
@MaheshHuddar
Жыл бұрын
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Thank you so much sir❤🙏
@MaheshHuddar
5 ай бұрын
Welcome Do like share and subscribe
Thank you so much!
@MaheshHuddar
10 ай бұрын
Welcome Do like share and subscribe