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Table of contents
1.
Introduction
2.
Mean Shift Clustering
3.
Kernel Density Estimation
4.
Requirements for Mean Shift Clustering
5.
Advantages of Mean Shift clustering
6.
Disadvantages of Mean Shift Clustering
7.
Frequently Asked Questions
7.1.
What is mean shift clustering used for?
7.2.
Is mean shift density-based?
7.3.
Is mean Shift a clustering algorithm?
8.
Conclusion
Last Updated: Mar 27, 2024

Mean Shift Clustering

Author Tashmit
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Master Python: Predicting weather forecasts
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Ashwin Goyal
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Introduction

Unsupervised learning is a magnificent term, and it consists of a lot of algorithms. In this article, we're going to discuss the mean shift clustering. It lies under the category of Unsupervised Learning. It's a flexible and straightforward clustering technique with several advantages over other approaches.

Source: Link

Mean Shift Clustering

Mean shift clustering, also known as the mode-seeking algorithm, allocates the data points to the cluster sequentially by shifting towards the mode. Mode is known to be the highest density of data points in the region. 

The mean shift clustering does not need to specify the number of clusters in advance, unlike the K-means cluster

Source: Link

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Kernel Density Estimation

The initial step in applying Mean Shift clustering is to plot the dataset mathematically. 

Source: Link

Mean Shift Clustering created the concept of kernel density estimation. Suppose that the above dataset was sampled from a probability distribution. Kernel Distribution Estimation is a process to estimate the underlying distribution, also called the probability density function for a data set.

The kernel is a fancy mathematical word for a weighting function generally used in convolution. There are various types of kernels, but the most widely used is the Gaussian kernel. Adding up all individual kernels generates a probability surface. The resultant density function will vary depending on the kernel bandwidth parameter used. The above data points can be divided into two parts

  1. Surface plot

    Source: Link
  2. Contour plot

Source: Link

Requirements for Mean Shift Clustering

There are two mandatory requirements to execute mean Shift Clustering

  1. It is needed to ensure that our estimate is normalized.
  2. It is associated with the symmetry of our space.

 

We can satisfy these requirements with the help of the following mathematical implementation:

Source: Link

Advantages of Mean Shift clustering

  • It finds a variable number of modes
  • It is robust to outliers
  • General, application-independent tool
  • Just a single parameter where h has a physical meaning
  • It is model-free and doesn't assume any prior shape like spherical, elliptical, etc. on data clusters

Disadvantages of Mean Shift Clustering

  • The output depends on window size
  • The window size selection is not trivial
  • It is computationally expensive
  • It doesn’t scale well with dimension of feature space.

Frequently Asked Questions

What is mean shift clustering used for?

Mean shift clustering is an unsupervised learning algorithm mainly used for clustering. It is primarily used in real-world data analysis as it's non-parametric and doesn't need any predefined shape of the clusters.

Is mean shift density-based?

Mean Shift clustering is an unsupervised clustering algorithm that discovers spots in a smooth density of samples. It is a centroid-based algorithm that works by updating candidates for centroids to be the mean of the points within a given region.

Is mean Shift a clustering algorithm?

Mean Shift Clustering is a hierarchical clustering algorithm. Unlike supervised machine learning algorithms, clustering attempts to group data without having first been trained on labeled data. Clustering is used in various applications such as search engines, academic rankings, and medicine.

Conclusion

In this article, we have extensively discussed Mean Shift Clustering and its features. We hope that this blog has helped you enhance your knowledge regarding Mean Shift Clustering and if you would like to learn more, check out our articles here. Do upvote our blog to help other ninjas grow. Happy Coding!

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