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?
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?
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
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 ??????
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?
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)
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.
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???
