Mastering Data Clustering with Python: Discover the Magic of DB Scan | Machine learning in Hindi

Mastering Data Clustering with Python: Discover the Magic of DB Scan | Machine learning in Hindi

DBSCAN Clustering : The secret Weapon for data scientists !
Avoid Costly Mistakes: Mastering DBSCAN Clustering Tutorial

Unsupervised Learning : DBSCAN Clustering with Example and calculations !

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🎥 *In Today's Video:*
DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is a popular clustering algorithm that is great for identifying clusters of varying shapes and sizes in a data set, especially when there is noise. Here's a step-by-step explanation of how DBSCAN works, followed by an example. After that, I'll create a PowerPoint presentation for you.

Step-by-Step Explanation of DBSCAN
Choose two parameters: ε (eps) and the minimum number of points required to form a cluster (MinPts). ε is the radius to search for neighboring points, and MinPts is the threshold for the minimum number of neighbors within ε radius.

For each point in the dataset:

Find the neighbors: Determine how many points fall within the ε distance from it. This includes the point itself.
Identify core points: If a point has at least MinPts within its ε neighborhood, it's marked as a core point. Core points are essential for forming a cluster.
Border points: If a point has fewer neighbors than MinPts but is within the ε radius of a core point, it's a border point.
Noise points: If a point is neither a core nor a border point, it's considered noise.
Form clusters: Start with a random core point and gather all its directly reachable points within ε distance, including other core points. If a core point is reachable, include all its neighbors in the cluster. This process is recursive until no more points can be added to the cluster.

Assign border points: Border points are not used to expand clusters, but they are assigned to one or more clusters based on their core point neighbors.

Repeat: Continue the process for all points in the dataset. Each point will be either part of a cluster or marked as noise.

Example
Imagine a dataset of points on a two-dimensional plane:

Parameters: ε = 2 units, MinPts = 3.
Points A, B, and C are close together; D and E are close but far from A, B, C; F is isolated.
A, B, and C form a cluster because each has at least 2 other points within a radius of 2 units (satisfying MinPts).
D and E form another cluster for the same reason.
F is marked as noise because it doesn't have enough neighbors.
This simple example illustrates how DBSCAN can identify clusters and noise within a dataset.

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