Mean Shift Clustering

Kernel Density Estimation

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

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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
   
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2. Contour plot

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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:

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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!