Clustering with DBSCAN, Clearly Explained!!!

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Information Clustering with DBSCAN, Clearly Explained!!!

Title	:	Clustering with DBSCAN, Clearly Explained!!!
Lasting	:	9.30
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Comment from : StatQuest with Josh Starmer

Josh's little bams catch me completely off guard
Comment from : Rich Huw

Best❤❤❤
Comment from : Fan

Dam Thank you, your clip is better explain than ChatGPT gave me!
Comment from : Jay Sirabhop

This explanation is so intuitive and amazing Thank you very much :)
Comment from : Bhavay Singhal

That was cool 👍
Comment from : zeynab Javid

Amazinggggg! Simple, clear and engaging content! Thank you
Comment from : Sruthi A

Mate i just love your channel The visual, the quality of the explainations, the shameless promotion humor haha brI just whished there were some french speaking channels as great as yours( French being my native language)brYou english is so clear that i get most of it, even if certain terms are a bit challenging for me People would love education if most teachers were like you !brAnyway keep up the great work :)
Comment from : Adrien Veres

Once we get the clusters out of algorithm, Is there any way to one level further down ? To find subclusters within clusters ?
Comment from : Archit Khanna

very well explained
Comment from : ////

That's awesome! Can you please make a video about Spectral Clustering as well?
Comment from : kimi101197

"shameless self promotion" xD
Comment from : Satya Srujan

So DBSCAN works on multi-dimensional space the same way? The circles are multi-dimensional?
Comment from : James Robertson

Thanks❤
Comment from : Neha Bhavsar

what if the size of the point is different? what are the options to use this methodology in that case ?
Comment from : Mohazzam saeed

Josh, thank you so much for all those videos U have no idea how mych u help us out! And if I may: could u do one about the Louvain clustering?
Comment from : Guilherme Afonso Vergara

Really nice explanation!
Comment from : Anthony Kin Ruiz Calvo

what a entry 😁
Comment from : Ariful Islam

how does DBSCAN work on multi dimensional data?
Comment from : XEQUTE

Bam! Brain washed by that Double Bam!
Comment from : Mingrui Zhang

Dat song 😆
Comment from : Bim Overbohm

Nice and simple😊
Comment from : Solo K

Speed 125, sometimes 15 feels slow with this guy
Comment from : n/a

Good InitiativeVery well explained
Comment from : vgreddy saragada

Have a shot everytime he says corepoint
Comment from : TheDestryCZ

Hey, thanks for the amazing video! Could you make an explanation video for HDBSCAN? I'm trying this out now and it looks very promising, but I'm having a harder time figuring it out than DBSCAN
Comment from : Maximillian Weil

THANK YOU FOR THIS! Please also make one for Ordering Points to Identify the Clustering Structure (OPTICS)
Comment from : red

I'm just binging all these classification algorithm videos, learning for my exam
Comment from : Erica Fey

Sir, how we decide if we should use DBSCAN or KNN for a clustering problem? is there any guideline or any steps to check ?
Comment from : Brad Fox

like the musical call back to sorting out sorting
Comment from : Thrashmetalman

helpful
Comment from : Vedant

thanks
Comment from : Vedant

Thank you !!!! you're great , I love your piu-piu-piu-piu 😁
Comment from : Anita Ona

Thanksssss!!!! and Bam!!
Comment from : Rishi Patel

This is the best and funniest video I have ever seen related to data mining concepts
Comment from : Nini Yaya

you are so smart
Comment from : Pranav Jain

Great Explanation So I just have one small doubt to confirm on, since you said outliers will be left over and not added to any cluster, so the DBSCAN algorithm is not sensitive to outliers Is this right?
Comment from : Mathesh

I can't believe how many of these fancy sounding algorithms are just coloring stuff that's close to eachother the same color
Comment from : Andy Dufresne

super nice explanation!
Comment from : fritz

Amazing explanation Josh Sir brplease make video for machine learning with python
Comment from : Tanveer Ahmad

the moment hearing the music I gave it a thumb up
Comment from : Sam Alexander

You are simply amezing Unertood in 9 min rather than spending hours on books Tripal Bam Thanks
Comment from : Pankaj Goikar

Can't express how much I enjoy your videos Thank you for the smile :)
Comment from : PeeDE

Well I can't not subscribe to you after this
Comment from : CamsterClips

Please it would be great if you can make a video of HDBSCAN, i couldn't find any resourse which explains it without going into cokplex maths
Comment from : Preet

great explanation, thanks!
Comment from : Mohamed El-Hadidy

How is it different from kNN?
Comment from : Adhiraj Nath

So there are 2 hyper-parameters in this algorithm : - the minimal number of neighbours to be a core point and the "radius" of a core point That's right ?
Comment from : Julien MOUCHNINO

Excelent!
Comment from : Victor Pinas Arnault

Thank you Joshhhh you are awesomeee!!!🥳🥳🥳🥳🥳🥳
Comment from : Lala Rzayeva

In the green cluster, why someone with a high weight low height is in the same cluster as someone with a low weight high height? I don't understand it
Comment from : Ku Qiu

Great!
Comment from : Sofian Meriane

Thank you
Comment from : om gorantiwar

00:05:15 what do you mean by "Core points that are close to the growing first cluster" ? Why not the upper-right core points?brNote that it's the most reviewed moment of the video 😉brGreat content nonetheless!
Comment from : Emilio Bognini

This is just brilliant! Hands-down the best explanation!
Comment from : O Ryo

God bless you! I’m looking forward to checking out these study guides as well 😄
Comment from : Almond Donut

Brilliant as always
Comment from : Marshel

Hi Josh, great video and clearly explained!! ;) How can we know whether the high-dimensional data contains nested clusters when we cannot visualize it? Do you recommend using PCA to still be able to visualize it and look whether the data is nested? Or is there another way to know when to use DBSCAN? Thanks in advance!!
Comment from : Wouter Regter

Thanks for the tutorial! Could you do a video about GaussianMixture please Thanks in advance!
Comment from : DemoProg

Very clear explanation with very cool visuals Thank you and keep it up :)
Comment from : Kevins35

As always, great job! Thank you very much!
Comment from : Beck

I love you
Comment from : Daniel Navarro

God bless you! :" )
Comment from : Almond Donut

quality stuff
Comment from : ROHITH REDDY

fabulous , The best explanation might be possible ; Thank you so much
Comment from : maryam zarabian

Never be shame of the self promotion It also help us to find more useful stuff :)
Comment from : Rui Li

Amazing explanation as always
Comment from : YASH SURANGE

I really liked the video, it's cool (subscribed)
Comment from : Valentina Savchenko

Great
Comment from : Mansi Sanwal

Thank you 💕
Comment from : Yamika CS

Hooray! Another awesome video :D!
Comment from : Banefane

Sir you have the gift of explaining complex things in simple manner
Comment from : Alpha Vijayan

I loved the short version of BAM! in this video it sounds so cute and non-core points are also called border points and outliers as noisebrthanks for introducing me with small bam and the waiting sounds I laughed out loud love your videos from India❤
Comment from : Moin Dalvi

Josh is the best at explaining complex statistics and machine learning topics!
Comment from : Annie Steenson

Thank you so much for your explanation! I tried to figure out what those core-points and border points are for quite some while but to no avail :( until i found your video today!
Comment from : sammi voon

Does someone have an implementation for dbscan (that doesn’t use libraries outside of numpy)on python or c++ that works for a lot of data?
Comment from : Avri Zaguri

Very well explained Thanks a lot!
Comment from : Aprilia Purwanto

Great entertainment+ knowledge bam!!!
Comment from : Surya

thank you sir, great video as always, a question please, how would you cluster geospatial dataset? you have a bunch of houses (with coordinates) and their prices, it becomes 3 columns dataset will you throw it directly to the DBSCAN?
Comment from : AI

⸻⸻⸻⸻⸻⸻⸻⸻⸻⸻⸻⸻⸻⸻⸻⸻⸻⸻⸻⸻⸻⸻⸻⸻⸻⸻⸻⸻⸻⸻⸻⸻⸻⸻⸻⸻⸻⸻⸻⸻⸻⸻⸻⸻⸻⸻⸻⸻⸻⸻

I need code a clustering algo for a dataset as hw, and I picked DBSCAN This video was perfect, thank you so much!
Comment from : ⸻⸻⸻⸻⸻⸻⸻⸻⸻⸻⸻⸻⸻⸻⸻⸻⸻⸻⸻⸻⸻⸻⸻⸻⸻⸻⸻⸻⸻⸻⸻⸻⸻⸻⸻⸻⸻⸻⸻⸻⸻⸻⸻⸻⸻⸻⸻⸻⸻⸻

Thank you for the clear explanation!
Comment from : shreeya joshi

every video is clearly explained
Comment from : Ethan Jiang

I like it how the core points join to form a cluster with pippeep pippeep pippip pippeep sound
Comment from : Vishnoo Rath

Having watched several hours of your videos now, have to say that this is the best one I've seen so far You made it so simple, so concise, that a 10 year old could understand it AND not get bored!brHonestly can't wait to see you raise the bar even higher than this
Comment from : Shane Glean

This explaination is SOOOOOO GOOD, THANK YOU
Comment from : chrono_

Awesome and easy understanding explanation! I actually was curious on how the metrics parameter (euclidean, manhattan, etc) influences the decision of the clustering process on this algorithm?
Comment from : Rafael Giacomazzi

Brilliant! Now could you do the same for Clustering with Optics!
Comment from : Sean Cody

BAAAMM
Comment from : Moises Diaz

This looks like corona virus spreading 🤣
Comment from : Ammar

Superb
Comment from : GCS

It's just awesome how nice you explained this algorithm Thank you so much for taking the time to summarize the information as good as you had
Comment from : Román Núñez Ortega

Thank you for your videos! I got 94 on my ML exam because I understood so much just by watching your explainations :)
Comment from : arzo9700

Very good explanation, very clear indeed If you allow a question: how can we know which method is more appropriate for our situation, k-means or DBSCAN? Thank you!
Comment from : Bogdan Anastasiei

I love to binge-watch the videos of this channel instead of Netflix
Comment from : Soumyadip Ghosh

Perfect timingbrCurrently writing my bachelor project and DBSCAN is part of itbrAs always, Josh Starmer is on point with the material I need! You sir certainly deserve the click of that subscription button I clicked long ago ❤️
Comment from : Total_DK

RIGHT IN TIME FOR MY EXAM brKing
Comment from : ace

This is the best explanation I ever seen
Comment from : Khaled Ihitt

Please consider making a video (or videos) on collaborative filtering and related topics of recommendation systems
Comment from : Krishna Chaitanya Velaga