conference paper
Considerably Improving Clustering Algorithms Using UMAP Dimensionality Reduction Technique: A Comparative Study
- Title
- Considerably Improving Clustering Algorithms Using UMAP Dimensionality Reduction Technique: A Comparative Study
- Editor(s)
- El Moataz, Abderrahim
- Mammass, Driss
- Mansouri, Alamin
- Nouboud, Fathallah
- Date
- 2020
- Is Part Of
- Image and Signal Processing
- In Series
- Lecture Notes in Computer Science
- Pages
- 317-325
- Language
- eng
- Publisher
- Springer International Publishing
- Place Published
- Cham
- ISBN
- 978-3-030-51935-3
- Short Title
- Considerably Improving Clustering Algorithms Using UMAP Dimensionality Reduction Technique
- Abstract
- Dimensionality reduction is widely used in machine learning and big data analytics since it helps to analyze and to visualize large, high-dimensional datasets. In particular, it can considerably help to perform tasks like data clustering and classification. Recently, embedding methods have emerged as a promising direction for improving clustering accuracy. They can preserve the local structure and simultaneously reveal the global structure of data, thereby reasonably improving clustering performance. In this paper, we investigate how to improve the performance of several clustering algorithms using one of the most successful embedding techniques: Uniform Manifold Approximation and Projection or UMAP. This technique has recently been proposed as a manifold learning technique for dimensionality reduction. It is based on Riemannian geometry and algebraic topology. Our main hypothesis is that UMAP would permit to find the best clusterable embedding manifold, and therefore, we applied it as a preprocessing step before performing clustering. We compare the results of many well-known clustering algorithms such ask-means, HDBSCAN, GMM and Agglomerative Hierarchical Clustering when they operate on the low-dimension feature space yielded by UMAP. A series of experiments on several image datasets demonstrate that the proposed method allows each of the clustering algorithms studied to improve its performance on each dataset considered. Based on Accuracy measure, the improvement can reach a remarkable rate of 60%.
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