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In this video on DBSCAN Clustering Algorithm, you will learn about one of the popular clustering algorithms, DBSCAN Clustering, which forms clusters based on the density of data points. In this video on Density Based Clustering, we will explore what DBSCAN clustering is, and how it creates clusters based on higher data point density, marking the rest as noise. Additionally, we'll also look into the implementation of how DBSCAN Clustering Algorithm forms clusters in arbitrary shapes that K-Means and Hierarchical Clustering Algorithms cannot. So just stay tuned with us until the end.

🔵 Following topics are covered in this session:
00:00 - Introduction to DBSCAN Clustering Algorithm
00:45 - What is the Density-Based Clustering?
02:20 - Epsilon and MinPoint
03:28 - Core Point, Border Point, and Outlier Identification DBSCAN Clustering
07:09 - How Density-Based Clustering Works?
09:26 - Python Implementation of DBSCAN Clustering
10:32 - Plotting Data Points in Circular Arrangement
12:00 - Training K-Means Model for Arbitrary Data
18:11 - Visualization of Clusters - K-Means Model
19:20 - Training Agglomerative Hierarchical Model for Arbitrary Data
24:56 - Visualization of Clusters - Hierarchical Clustering Model
26:14 - Training DBSCAN Clustering Model for Arbitrary Data
30:10 - Visualization of Clusters - DBSCAN Clustering Model
31:00 - Analyzing How DBSCAN is Better for Arbitrary Data


✅ What is the DBSCAN Clustering Algorithm?
DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is a clustering algorithm that groups data points based on their density. It identifies core points with a sufficient number of neighbors, expands clusters from these points, and designates points with lower density as outliers. DBSCAN is effective for discovering clusters of arbitrary shapes and handling noise in datasets.

✅ Why DBSCAN Clustering Algorithm is better than K-Means Algorithm?
DBSCAN Clustering Algorithm surpasses K-Means in handling clusters of arbitrary shapes and varying densities. Unlike K-Means, DBSCAN does not require specifying the number of clusters in advance, automatically adapting to the data's structure.


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