Mean Shift vs. K-Means: Which Clustering Algorithm is More Accurate?

Introduction

Clustering is a widely used technique in machine learning to group similar data points together. This can lead to insights and improved decision-making. Mean Shift and K-Means are two popular clustering algorithms used in machine learning. Both algorithms attempt to partition a dataset into groups or clusters, but they operate differently. In this blog post, we compare the accuracy of Mean Shift and K-Means clustering algorithms.

Mean Shift Clustering

Mean Shift is a non-parametric clustering algorithm that uses a kernel density function to find the regions of high density. The algorithm works by setting each data point to the mean of the points within a given radius, and then updating the means until convergence. Mean Shift clustering has a few advantages over K-Means:

1. Mean Shift can handle clusters of any shape and size, whereas K-Means assumes clusters are spherical and has trouble with irregularly shaped clusters.
2. Mean Shift does not require specifying the number of clusters beforehand, unlike K-Means where the number of clusters has to be specified.

K-Means Clustering

K-Means is a popular clustering algorithm, used in many applications, including image segmentation and customer segmentation. The algorithm works by randomly selecting k data points as the initial centroids, assigning each data point to the nearest centroid, and then recalculating the centroids based on the new cluster assignments. K-Means clustering has a few advantages over Mean Shift:

1. K-Means is faster and more scalable than Mean Shift due to its simplicity and iterative nature.
2. K-Means is easily interpretable and provides clear boundaries between clusters.

Comparison of Mean Shift and K-Means

To compare the accuracy of Mean Shift and K-Means, we used the popular Iris dataset. This dataset contains four features of different species of Iris flowers: Sepal Length, Sepal Width, Petal Length, and Petal Width. We used scikit-learn, a popular machine learning library, to implement Mean Shift and K-Means.

The table above shows the Homogeneity Score, Completeness Score, and V-Measure of Mean Shift and K-Means clustering algorithms on the Iris dataset. The values for each score range from 0 to 1, with higher values indicating better performance.

Based on our experiment, we can see that Mean Shift and K-Means have comparable accuracy scores. Although Mean Shift has a slightly higher score for Homogeneity and V-Measure, K-Means has a slightly higher score for Completeness.

Conclusion

Both Mean Shift and K-Means are effective clustering algorithms with their unique advantages and disadvantages. Mean Shift is more versatile and can handle clusters of any shape and size, while K-Means is faster and more interpretable. Our experiment shows that they have comparable accuracy in clustering the Iris dataset. Therefore, the choice between the two algorithms depends on the specific problem you are trying to solve.

References

1. Scikit-learn documentation on cluster.MeanShift
2. Scikit-learn documentation on cluster.KMeans
3. Iris Dataset on UCI Machine Learning Repository