Mean Shift Clustering | How Mean Shift Clustering Works | Scikit Learn Tutorial | Intellipaat
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In this video, we will be going to focus on one of the types of clustering known as Mean Shift Clustering. Mean shift clustering is a non-parametric clustering algorithm that aims to discover "blobs" or dense regions in a dataset. We will cover what Mean Shift Clustering is, and the work behind this clustering algorithm, and towards the end, we will also implement it using the scikit-learn library. So, please stay tuned with us until the end.
🔵 Following topics are covered in this session:
00:00 - Introduction
01:22 - What is Mean Shift Clustering?
05:49 - How does Mean Shift Clustering Work?
07:58 - Advantages and Disadvantages of Mean Shift Clustering
08:49 - Applications of Mean Shift Clustering
09:26 - Mean Shift Clustering Implementation
✅ What do you mean by clustering?
Clustering is a data analysis technique that groups similar data points together. It's like sorting a pile of socks by color or size. Clustering algorithms can handle unlabeled data, meaning they don't need to know the categories in advance. They work by finding patterns and similarities in the data and grouping points based on those similarities.
✅ What is the mean shift clustering algorithm?
Mean Shift Clustering is a non-parametric clustering algorithm that groups data points based on their density. It works by iteratively shifting data points towards the mode (i.e., the highest density) of the distribution of points within a certain radius. The algorithm stops when the points converge to a local maximum of the density function. These local maxima represent the clusters in the data.
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