DBSCAN Clustering explained | How DBSCAN clustering Works | Density based clustering

DBSCAN Clustering explained | How DBSCAN clustering Works | Density based clustering
#DBSCANClustering #UnfoldDataScience
Hello ,
My name is Aman and I am a Data Scientist.
About this video:
In this video, I explain about DBSCAN clustering. I explain step by step process of DBSCAN clustering. I explain how density based clustering works. I explain how density based clustering works with example.
Below topics are explained in this video:
1. How DBSCAN clustering works
2. Density based clustering explanation
3. How density based clustering works step by step
4. What is epsilon in density based clustering
5. what is core point in DBSCAN clustering
6. What is border point in Density based cluster
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Predictive Analytics By By Eric Siegel - amzn.to/3nsQftV
Data Science for Business By Foster Provost - amzn.to/3ajN8QZ
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Naked Statistics By Charles Wheelan - amzn.to/3gXLdmp
Practical Statistics for Data Scientist By Peter Bruce - amzn.to/37wL9Y5
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Introduction to machine learning by Andreas C Muller - amzn.to/3oZ3X7T
The Hundred Page Machine Learning Book by Andriy Burkov - amzn.to/3pdqCxJ
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[@sumitjain1655]

I'm doing Business analytics course and I refer to you video for understanding. Plz keep up the great work of enlightening us.

[@UnfoldDataScience]

Thanks Sumit. Good luck with your course.

[@faizainkorea]

Well explained

[@UnfoldDataScience]

Thanks Faiza.

[@sumitjain1655]

Again nailed the topic. This is amazing how simply you have managed to explain the the concept

[@UnfoldDataScience]

Thanks Again Sumit. Please share with your friends who might get benefitted :)

[@Mars7822]

Nice and brilliant class sir.

[@luamalem2617]

Thank you so much. This is clear and on point. Subscribed!

[@UnfoldDataScience]

Thanks Luam 😊

[@vallimuthaiyah5098]

Excellent Explanation!! Please upload more videos of this similar kind sir..

[@UnfoldDataScience]

Thanks Valli. Sure :)

[@aiuslocutius9758]

Thank you for the detailed explanation!

[@UnfoldDataScience]

Welcome

[@ehsanmobinipour6825]

very good

[@babaabba9348]

best explanation

[@anifminhazkhan4143]

your explanation is amazing man... keep going!

[@UnfoldDataScience]

Thanks a lot.

[@muhammedthayyib9202]

Nice and sweet explanation. I shared with my friends. Thank you Aman

[@UnfoldDataScience]

Thanks Thayyib

[@user-bm5yt5zj1v]

Great explanation. Thank you!

[@UnfoldDataScience]

Welcome

[@SuperAstrax111]

Thank you sir

[@UnfoldDataScience]

Welcome Asres.

[@rds9815]

HI
its very nice the way your explaing the topics really i love it thanks for the video

[@UnfoldDataScience]

You are most welcome. Pls share with friends as well

[@sarthak_yt_2009]

Really very nice teaching.....

[@UnfoldDataScience]

Keep watching Sarthak

[@sheruloves9190]

Thanks a lot..

[@UnfoldDataScience]

Welcome.

[@sachinladdha]

how to use DBSCAN in case of multiple features? Is there any technique to use only few features or all feature but less important with very small weightage?

[@sandipansarkar9211]

FINISHED WATCHING

[@rajareddypandiri2226]

Excellent explanation 👌

[@UnfoldDataScience]

Glad you liked it Raja.

[@btkcodedev]

Underrated Channel, Plus one sub

[@UnfoldDataScience]

Thanks a lot Bala. Your words are my motivation

[@kar2194]

Hi sir, a great thanks from me. A general question sir, I have performed DBSCAN, Fuzzy, and K-means clustering, how would I suggest which algorithm is best for the data? If the dataset is quite mess, large scale 10k rows, and skewed with big amount of outliers

[@christygeorge73]

Please put something for deep learning like cnns rnns and examples for those

[@shubhamchoudhary5461]

lucid explanation

[@UnfoldDataScience]

Thanks Shubham.

[@datascienceworld7041]

If we give Epsilon=1 then it will randomly draw a circle on a particular data point and make its a circle with radius 1 ,so the core point is also chosen randomly ??????

[@austinmark242]

Can you do a playlist on computer vision feature extraction techniques like hog sift (svm+hog), etc

[@UnfoldDataScience]

Hi Augustine, I will try to add. Thanks for suggesting.

[@navneetgupta4669]

How to select the best algorithm for the data by looking at the data?
This the question that I faced in many interviews.
Can you please make a video on it?

[@UnfoldDataScience]

This can not be done upfront without digging deep into data however it also depends on many factors. I will explain in one video separately.

[@nareshjadhav4962]

Excellent explanation, but one question..how can we evaluate DBSCAN , is there any test like we evaluate k- means ckuster by silhouette test?

[@UnfoldDataScience]

Yes Naresh, I ll cover it in my upcoming video.

[@ranajaydas8906]

Sir please upload a video on PCA next. 🙏

[@UnfoldDataScience]

I will upload Ranajay.

[@ravanshyam7653]

noise points are not consider in any clsuters right??? if new data is added ,then that data points form a cluster around noise point and then that noise point is also includes in a cluster or not???.then accuary of algortm changes or remains constant???

[@UnfoldDataScience]

Hi Ravan, Noise will not be part of any cluster in any case. There is nothing like "Accuracy" in unsupervised ML.

[@ravanshyam7653]

@@UnfoldDataScience thanks ❤️

[@anushamv3190]

Hello sir,
Which algorithm works well for customer segmentation wrt Recency, Frequency, Monetory?
And is necessary to apply all the algorithms that is Kmeans, Dbscan, hier to the dataset and then come yo conclusion.

[@UnfoldDataScience]

Hi Anshu, RFM is a good basic point to start with however we should try to fit data with advance techniques.

[@nikhildesai2460]

Hi Aman,
Thanks for the explanation, but my doubt is how cluster can be decide which point needs to take as a core point? What is the math behind that?

[@UnfoldDataScience]

For each point xi, compute the distance between xi and the other points. Finds all neighbor points within distance eps of the starting point (xi). Each point, with a neighbor count greater than or equal to MinPts, is marked as core point or visited.(copied from web as It was quicker)

[@karthickkarthi2401]

sir doubt on stats why are we converting the skewed distrubution to Gaussian distrubution?

[@UnfoldDataScience]

Hi karthick, this we do typically in regression models as that is one of the assumption.

[@nayanranjandas1854]

Sir please upload a video on Spectral Clustering next.

[@nayanranjandas1854]

Sir, I want to add another point, it will be really beneficial if you make a separate video on unnormalized and normalized spectral clustering.

[@UnfoldDataScience]

Sure Nayan, thanks again.

[@surajgupta-dc2ue]

Can you pls make video on birch algorithm? Plz sir

[@UnfoldDataScience]

Thanks for suggesting Suraj gupta :)

[@SuperPhysicsgeek]

what is eps again can spell out didnt really catch the pronocuation?

[@UnfoldDataScience]

Hi David, can you tell me which part of the video.

[@sauravksingh]

Can you also explain Isolation FOrest

[@UnfoldDataScience]

Will do Saurav. Thanks for suggesting.

[@ravanshyam7653]

sir if interviewer asks differnetiate blw centroid and core point.........how can we proceed?

[@UnfoldDataScience]

In DBSCAN its all about, core/border/noise points. Centriod is defined in K-means not DBscan

[@Tetraone597]

GRANDEEE

[@abhinavkhandelwal1045]

I have a question, which algorithm to use in varying density if not DBSCAN?

[@UnfoldDataScience]

Try with k-means or hier