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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dbscan in data mining,
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[@WojciechBrzoska-w2s]

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]

Thank You
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[@ashishkumar-c3d6m]

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 TH-camrs 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]

Thank You
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[@praneshchow]

Nice slide and explanation. You just easily overview the topic within 11 minutes. Thanks ❤

[@MaheshHuddar]

Welcome
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[@hoyintam1702]

Great video with a nice example, detailed calculation and clear explanation!

[@MaheshHuddar]

Thank You
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[@shalinisivasankar5172]

Watching just 10 mins before exam😮‍💨

[@sarayu915]

tomrw is my exam thank you sirr well explained my professor took hours to explain this 😄😄

[@MaheshHuddar]

Welcome
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[@sankar2885]

யாரு சார்?நீங்க...ஏழேழு ஜென்மம் நீங்க நல்லாருக்கனும் சார்❤

[@meowzerilla]

amazing channel with exceptional explanation. always look for your videos when im searching a topic on youtube

[@MaheshHuddar]

Thank You
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[@sleepdeprived1266]

That was awesome explanation. Just a question, from what i understand once a point has been assigned to a cluster you cannot reassign it to the next one so i believe in 10:18 you should just keep 10 and 12 in seperate cluster and not mix with 11 and 2 which already belong to a cluster

[@AbhishekTiwari-uu8pq]

you can cross the clusters, there is nothing wrong with that. 2 can be a part of multiple clusters

[@aderahmatiskandar33]

Thank you very much, it's really interesting and understood easily for your exolanation about DBScan with the real case

[@MaheshHuddar]

Welcome
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[@bramsetyadji2881]

Thank you very much!!! It is very clear and concise explanation

[@MaheshHuddar]

You are welcome!
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[@theatregare2157]

You are the man bro, amazing job

[@MaheshHuddar]

Glad it helped
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[@HelloBangladesh389]

Thanks..........
now it's easy for me

[@radwataher6056]

very good explanation and slides, Thanks

[@MaheshHuddar]

Welcome
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[@arunkumarias5372]

Thank you so much sir❤

[@MaheshHuddar]

Most welcome
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[@SAIRUI_Hydraulic]

Great Teaching.
Thank you

[@MaheshHuddar]

Welcome
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[@ragennepal1646]

Thank you sir ❤❤❤

[@aniruddhsingh5280]

Excellent explanation sir !!

[@MaheshHuddar]

Thank You
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[@nithinvasamsetti]

You are blowing my brain ❤❤❤ amazing explanation

[@MaheshHuddar]

Thank you so much
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[@thedarkknight-m7n]

super explaination

[@shk_innovates]

if two clusters merge together, do they become a single cluster or remain different clusters??

[@VANDANAMEHNDIRATTASCHOOLOFSCIE]

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]

Ok

[@aenasaluja5128]

amazinggggggggggggggggggggg
thankyou sir

[@usmanyousaaf]

Well explained 👍

[@MaheshHuddar]

Thank you
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[@ruysilva298]

Thank you a lot.

[@MaheshHuddar]

Welcome
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[@AbhishekTiwari-hw9kw]

Thank you sir

[@MaheshHuddar]

Welcome
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[@jeodezman32]

Excellent explanation, thank you so much!

[@MaheshHuddar]

Welcome
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[@sowjanyanaga839]

Super explanation sir

[@MaheshHuddar]

Thank You
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[@natashaasrani9521]

Respected Sir,thankyou for the videos but a doubt that P8 has distance less than 1.9 with only P5 but P7 and P8 doesnt complete the epilson criteria then why you are putting them under one cluster

[@suriya4250]

If the minpts and e is not given what can we do sir. can we determine with heuristic method

[@SreejaBollam]

Can u explain graph how to draw it such as points

[@mayi344]

the points given in the question were plotted you can find here : 0:10

[@CfgCff-g9x]

Thanks

[@giriprasad5221]

Great explanation sir

[@MaheshHuddar]

Thank You
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[@dark__cherry5307]

❤

[@satyaswarupa6839]

sir could you please explain that......... how would you plot the graph points

[@skarthikeya5285]

from 0:10 ro 0:40 the points shown numerically here are just plotted on graph

[@jimintata5091]

​@@skarthikeya5285 u cleared my doubt TQ

[@ironman5255]

Tq

[@devfusion-8718]

​@@skarthikeya5285Tq

[@kushagramishra9257]

can the clusters overlap ?

[@anastariq8464]

how do you scatter the points on graph at the end

[@rohannaidubadarla280]

A small doubt, now the point 11 belongs to which cluster??

[@realpg4643]

amazing sir🤩

[@MaheshHuddar]

Thanks a lot 😊
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[@BILLARAMPRASADREDD]

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]

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

[@harisubedi3235]

ammamaa bhagwan ho mero lagi yo sir :)

[@Lost_User888]

Sir, graph me centre points 2,5,11 or inke nearest points ki plotting randomly kri hai????

[@bang-n6k]

no, it is done according to the coordinates data given in the question. Like - P1:(3,7), P5:(7,3)

[@Lost_User888]

@bang-n6k ok thankuu ☺

[@fintech1378]

excellent lesson, i will have an exam in a few days where I cannot code, where can I find similar practice questions?

[@MaheshHuddar]

Numerical examples..?

[@fintech1378]

@@MaheshHuddar yes, both supervised and unsupervised, not in python

[@lohotbrijesh]

thank you

[@MaheshHuddar]

Welcome
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[@suryaprasadsahoo9617]

👍

[@MaheshHuddar]

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[@NIKHILSHARMA-q6s9z]

what to do if the distance is equal to epsilon?

[@sravansunkara]

Thank you so much!

[@MaheshHuddar]

Welcome
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[@younus.athlete]

Sir ye slides aapkp kha sey milee, cause ye same slided hamari ma'am bhe copy ki hain. can you please tell me

[@Nikhil.Rebelling]

Sir can we use Manhattan distance formula ?

[@MaheshHuddar]

Yes
You can use any distance metric

[@Nikhil.Rebelling]

@@MaheshHuddar Thank You Sir For Your Quick Response !!

[@tharunganesh98]

But here point 2 and 11 belongs to two different clusters? how can we explain that?

[@ChodagiriYugandhar]

👏👏👏

[@MaheshHuddar]

Thank You
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[@tmjackff7809]

CLARANS

[@deepaksonawane2501]

How to form clusters for points with more than two coordinates?

[@deekshaPatima]

How the graph is pointed can anyone explain

[@donivanka]

First all given points (x-axis,y-axis) in the question were plotted on graph. Then clustered according to the core points

[@rajeevlochanmedisetti5664]

Can we use manhattan formula insted of euclidean

[@MaheshHuddar]

Yes you can use any distance metric

[@sapavathprasannakumari]

Please provide the pdf

[@PavanPavan-c2p]

🙏🏼🫡

[@TienTran-ni5vn]

may I have your file ppt of this video?

[@rohanm2943]

Got this question for 10 marks... and spent whole 1 hr just to calculate them all🥲

[@arpitarya_101]

Seriously!!

[@rohanm2943]

@arpitarya_101 fr

[@minatharu512]

Thank you so much sir❤🙏

[@MaheshHuddar]

Welcome
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[@hitendusseja6615]

Thank you

[@shalabhagarwal]

Thank you so much!

[@MaheshHuddar]

Welcome
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