Deep Embedded Clustering (DEC) is a method that simultaneously learns feature representations and cluster assignments using deep neural networks. The paper proposes DEC as a solution to the problem of learning a feature space for clustering without relying on labeled data. DEC uses a parameterized non-linear mapping from the data space to a lower-dimensional feature space, where it iteratively optimizes a clustering objective. The method is trained using stochastic gradient descent (SGD) via backpropagation on a clustering objective, which is parameterized by a deep neural network. DEC is evaluated on image and text datasets, showing significant improvements over state-of-the-art methods in terms of accuracy and running time. The method is also less sensitive to hyperparameter choices compared to other clustering methods. DEC is implemented using a Caffe-based framework and is available at https://github.com/piiswrong/dec. The paper also discusses related work in clustering, including k-means, Gaussian Mixture Models, spectral clustering, and other clustering algorithms. The experiments show that DEC outperforms other methods in clustering accuracy and is more robust to hyperparameter changes. DEC is also able to process large datasets efficiently due to its linear complexity in the number of data points. The paper concludes that DEC provides a powerful and efficient method for clustering data without the need for labeled data.Deep Embedded Clustering (DEC) is a method that simultaneously learns feature representations and cluster assignments using deep neural networks. The paper proposes DEC as a solution to the problem of learning a feature space for clustering without relying on labeled data. DEC uses a parameterized non-linear mapping from the data space to a lower-dimensional feature space, where it iteratively optimizes a clustering objective. The method is trained using stochastic gradient descent (SGD) via backpropagation on a clustering objective, which is parameterized by a deep neural network. DEC is evaluated on image and text datasets, showing significant improvements over state-of-the-art methods in terms of accuracy and running time. The method is also less sensitive to hyperparameter choices compared to other clustering methods. DEC is implemented using a Caffe-based framework and is available at https://github.com/piiswrong/dec. The paper also discusses related work in clustering, including k-means, Gaussian Mixture Models, spectral clustering, and other clustering algorithms. The experiments show that DEC outperforms other methods in clustering accuracy and is more robust to hyperparameter changes. DEC is also able to process large datasets efficiently due to its linear complexity in the number of data points. The paper concludes that DEC provides a powerful and efficient method for clustering data without the need for labeled data.