DBSCAN Clustering Algorithms | Density Based Clustering | How DBSCAN Works | CampusX

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In today's video, we dive deep into the world of clustering with a focus on DBSCAN (Density-Based Spatial Clustering of Applications with Noise). Whether you're a data science enthusiast, a student, or a professional looking to enhance your machine learning skills, this video is for you!

🚀 Why DBSCAN?
DBSCAN is a powerful tool in your clustering toolkit, providing robust performance in identifying clusters of varying shapes and handling outliers effectively.

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⌚Time Stamps ⌚

00:00 - 00:31 - Intro
00:32 - 03:59 - Why DBSCAN?
04:00 - 06:28 - What is density based clustering?
06:29 - 08:41 - Epsilon Example
08:42 - 14:12 - 3 Types of points in DBSCAN
14:13 - 17:58 - Density connected points
17:59 - 25:20 - DBSCAN Algorithm
25:21 - 28:55 - DBSCAN Demo
28:56 - 32:40 - Limitations
32:41 - 34:00 - Visualization Tool
34:01 - 34:15 - Outro

✨ Hashtags✨
#DataScience #MachineLearning #Deeplearning #CampusX #DBSCAN #Clustering #AlgorithmExplained

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sandeeppolaki

00:02 DBSCAN is important due to flaws in K Means clustering algorithm
02:24 DBSCAN is a density based clustering algorithm
07:58 Understanding density and key DBSCAN hyperparameters
10:37 Explaining core points, border points, and nose points in DBSCAN clustering
17:00 Understanding DBSCAN Algorithm in four steps
19:50 DBSCAN Step 1: Identifying core, border, and noise points
24:16 DBSCAN clusters points based on density
26:42 DBSCAN focuses on density for clustering
30:58 DBSCAN clustering has limitations in handling clusters of different density and cannot make predictions.
33:02 DBSCAN algorithm can be visualized and tested with different Min Points values
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