This paper introduces Deep Embedded Clustering (DEC), a method that jointly learns feature representations and cluster assignments using deep neural networks. DEC maps data from a high-dimensional space to a lower-dimensional feature space, optimizing a clustering objective iteratively. The authors demonstrate significant improvements over state-of-the-art methods in both accuracy and running time on image and text datasets, showing that DEC is more robust to hyperparameter choices. The method is evaluated on MNIST, STL, and Reuters datasets, and compared with $k$-means, LDGMI, and SEC. DEC's performance is superior, and it scales well to large datasets due to its linear complexity in the number of data points. The paper also discusses the assumptions, contributions, and robustness of DEC, highlighting its effectiveness in handling imbalanced data and determining the optimal number of clusters.This paper introduces Deep Embedded Clustering (DEC), a method that jointly learns feature representations and cluster assignments using deep neural networks. DEC maps data from a high-dimensional space to a lower-dimensional feature space, optimizing a clustering objective iteratively. The authors demonstrate significant improvements over state-of-the-art methods in both accuracy and running time on image and text datasets, showing that DEC is more robust to hyperparameter choices. The method is evaluated on MNIST, STL, and Reuters datasets, and compared with $k$-means, LDGMI, and SEC. DEC's performance is superior, and it scales well to large datasets due to its linear complexity in the number of data points. The paper also discusses the assumptions, contributions, and robustness of DEC, highlighting its effectiveness in handling imbalanced data and determining the optimal number of clusters.