DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is a density-based clustering base technique. It can find clusters of various forms and sizes in a big amount of data that includes noise and outliers.
The goal of DBSCAN is to locate data point neighbourhoods that exceed a specific density criterion. The radius of the neighbourhood (eps) and the minimum number of neighbors/data points (minPts) within the radius of the neighbourhood define the density threshold.
DBSCAN is a clustering method used in machine learning to distinguish high-density clusters from low-density clusters.
DBSCAN has two parameters: (eps) and the minimum number of points needed to build a dense zone (minPts). It begins with a random starting spot that has never been visited before. The -neighborhood of this point is retrieved, and if it contains enough points, a cluster is formed.
Advantages. DBSCAN does not require the number of clusters to be specified in advance. DBSCAN works effectively with clusters of any shape. DBSCAN understands noise and is resistant to outliers.
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Hi Kushal ! Your way of teaching is extremely helpful and you are one of the best teacher in the world.
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Aniket Kumar prasad
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Very helpful and easy to understand all the concepts, best teacher for learning ML.
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Rishu Shrivastav
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explained everything in detail. I have a question learnvern provide dataset , and ppt ? or not?
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VIKAS CHOUBEY
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very nicely explained
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Vrushali Kandesar
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Awesome and very nicely explained!!!
One importing thing to notify to team is by mistakenly navie's practical has been added under svm lecture and vice versa (Learning Practical 1)
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Joydeb
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Awesome Course sir and your teaching style is very GOOD.
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Shaga Chandrakanth Goud
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Hi Kushal ji, Thanks a lot for a very good explanation. I have doubts about where we can get the dataset that you explained in the video. Can you make it available in resource ,so that we can downld
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