DBSCAN Algorithm In Python | DBSCAN clustering Algorithm example| Density based clustering python

[@kalpanapatil1028]

Helpful video. Thanks Aman

[@borjazarauz4515]

Super helpful videos, Aman. Thanks!

[@muhammedthayyib9202]

To find optimum min_sample, I did't see any proper method. But journal says that. For large dataset this should be large. If data is noisy, choose large min_sample. For 2-dimensional data, default is 2. If data has more than 2 dimensions, choose 2*dim, where dim= the dimensions of your data set. In my common sense I think for high density eps (distance) will be less. Min_sample does not make problem. But for less dense min_sample should not be less. Aman, is there any other way to determine ? Thank you Aman

[@beautyisinmind2163]

Which paper said that min_samples = 2*dim? can you provide the reference?

[@vallimuthaiyah5098]

Thank you sir for valuable information on DBSCAN clustering 👍.. we can find out optimal number of clusters using elbow method or sum of squares method

[@UnfoldDataScience]

Thanks Valli,

[@nathanjones639]

@@UnfoldDataScience 1- Run the DBSCAN Algorithm on a suitable dataset and determine the success criteria suitable for your algorithm. How do we find the values? 2-Hyper parameter tuning of your DBSCAN Algorithm and How do we make the success criteria of both the training and test set for the best model? 3-please,Can you share the python code of these questions with me?

[@nathanjones639]

@@UnfoldDataScience example on dataset please 1- Run the DBSCAN Algorithm on a suitable dataset and determine the success criteria suitable for your algorithm. How do we find the values? 2-Hyper parameter tuning of your DBSCAN Algorithm and How do we make the success criteria of both the training and test set for the best model? 3-please,Can you share the python code of these questions with me?

[@sandipansarkar9211]

FINISHED WATCHING

[@nayanranjandas1854]

Thank you sir for your valuable information on DBSCAN clustering. Besides, Sir please upload a video on Unnormalized spectral clustering with algorithm steps.

[@UnfoldDataScience]

As soon as possible Nayan.

[@nayanranjandas1854]

@@UnfoldDataScience Kindly sir, if you can upload the content (Unnormalized spectral clustering with algorithm steps) as early as possible, it will be a great help.

[@kazbekasanov9725]

Hello , can u please tell me , kmeans.cluster_centers_ why it gives so much centers , like shouldn’t be one centroid that was defined in the end of the algorithm , one centroid for one cluster and another centroid for another one

[@UnfoldDataScience]

It assignes all data points to one cluster.

[@beautyisinmind2163]

What about the Elbow mthod and gridsearch CV method for finding the EPS and Min_samples??

[@akashprabhakar6353]

Hi Aman, Can u pls elaborate what this parameter n_neighbors=2 doing here neigh = NearestNeighbors(n_neighbors=2)...The distances you have found is b/w some pairs but how that specific pairs are selected(neighbors=2 means the single nearest neighbour to each point ..isnt''t??) But when I am changing neighbors to 3,4,5, distance is not changing and is same...Kindly tell why is it so and is it still finding distance to nearest neighbour and whats the use of n_neighbors then?? Regards

[@adityasharma2667]

How to deal with categorical variable when running DBSCAN model

[@UnfoldDataScience]

Good question. I will discuss this topic in my next video.

[@adityasharma2667]

@@UnfoldDataScience Thanks Aman, I will be surely waiting for the video..

[@yasink18]

Sir can we use this DBScan to understand user click patter on website?

[@UnfoldDataScience]

yes

[@shahneelapitafi7406]

can we apply DBSCAN to Imagiary dataset ?

[@UnfoldDataScience]

Try

[@anojananantharajah2417]

Hello Sir, thank you for this clear video. I have a segmentation project on which I am well advanced but I would like to understand the main steps to follow and in which order. Would you have any video, idea or advice on which model to apply (with or without dimension reduction), how to compare them (silhouette score) in the best way etc. The notions that I could integrate in my project are the T-sne, the Pca, K-Means and Dbscan. I feel like I am doing the important steps but without necessarily having a rigorous and orderly plan. Thanks in advance

[@UnfoldDataScience]

It will be anything related to feature engineering then model training then prediction. Why are you using two clustering techniques like dbscan and k menas here?

[@balapranav5364]

Sir please make video on batch normalisation please

[@UnfoldDataScience]

I will add batch normalization video Bala.

[@JohnDoe-wi6nq]

Hello Thanks a lot for these videos. I love them the most. I've a time series data set with 5-10 features (all numerical). I've been using classification models to categorize in 10 different classes. I've done this categorisation myself to create the target classes and fitting classification models. Can I run a clustering algo on the featureset and see how the clusters are apearing on the input data only. If yes how? Basically I'm asking, can I approach a classification problem in clustering setting?

[@kripalsinghbamel9819]

Yes, you can try and validate if it works fine

[@nathanjones639]

1- Run the DBSCAN Algorithm on a suitable dataset and determine the success criteria suitable for your algorithm. How do we find the values? 2-Hyper parameter tuning of your DBSCAN Algorithm and How do we make the success criteria of both the training and test set for the best model? 3-please,Can you share the python code of these questions with me?

[@DaughterOfGodJG]

optimal value of min_sample= no of dimension+1 , eps= min_sample-1

[@DaughterOfGodJG]

wrong clustering = DBSCAN(eps=2, min_samples=3).fit(X) clustering.labels_ array([-1, -1, -1, -1, -1, -1], dtype=int64)

[@sane7263]

For new prediction, why not just compute the Euclidean distance between the new datapoint with all other datapoints, then find which datapoint is the closest. The new datapoint belong to the cluster of that nearest datapoint. What am I missing?🤔

[@yasink18]

It's the same rule.. DBScan also using the Euclidean distance

[@shalakhajadhav1289]

Waiting for naive Bayes.

[@UnfoldDataScience]

Ok Sure.