How Is Mean-Shift Clustering Better Than K-Means Clustering?

No need to specify the number of clusters (k) as a hyperparameter!

Rukshan Pramoditha
4 min readAug 4, 2024

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Image by Beth Thomas from Pixabay

Clustering is an unsupervised machine learning type which works with unlabeled data.

Clustering assigns similar data points into clusters (groups). When training, the goal is to find a cluster label for each data point. A well-trained clustering model should be able to assign new unseen data points in the same domain to the identified clusters.

K-Means and Mean-Shift are two popular clustering algorithms. As I have already discussed the K-Means algorithm in my article, Hands-On K-Means Clustering, today, more emphasis will be given to the Mean-Shift algorithm while we still compare both algorithms.

How Mean-Shift Clustering Works

The Mean-Shift algorithm assigns data points into clusters by iteratively shifting each data point towards the mode of the data points within a limited radius which is defined by the bandwidth hyperparameter. The mode defines the highest density area of data points in Mean-Shift clustering. The algorithm continuously performs the shifting process until all data points are assigned to clusters.

Advantages of Mean-Shift Clustering over K-Means…

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Rukshan Pramoditha
Rukshan Pramoditha

Written by Rukshan Pramoditha

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