Clustering algorithms are designed to deal with the datasets from a wide range of real-world applications based on the task. However, there are some situations where the clusters fail to maintain their performance due to mixed datasets problems. An active research area that is severely affected by these problems is the heart disease dataset. Deep learning methods, the state-of-the-art classifiers, with better learning procedures and computational resources, can fill these gaps. To improve the robustness of clusters, we propose a Constraint-Based Deep Convolutional Generative Adversarial Network (CB-DCGANs) framework for generating simulated data to augment the training set to improve the performance of the clustering algorithm. We evaluated the performance of Discriminatively Boosted Clustering (DBC) in detecting the clusters from given datasets. This study shows that using generative adversarial networks for clustering augmentation can significantly improve performance, especially in real-life applications.

聚类算法旨在根据任务处理来自大量实际应用程序的数据集。但是，在某些情况下，由于混合数据集问题，群集无法维持其性能。受这些问题严重影响的活跃研究领域是心脏病数据集。深度学习方法，最新的分类器，更好的学习程序和计算资源可以填补这些空白。为了提高聚类的鲁棒性，我们提出了一种基于约束的深度卷积生成对抗网络（CB-DCGAN）框架，用于生成模拟数据以增强训练集，从而提高聚类算法的性能。我们评估了判别增强聚类（DBC）从给定数据集中检测聚类的性能。