Abstract: Clustering is considered one of the most critical unsupervised learning problems. It endeavors to find an accurate structure in a collection of unlabeled data. In this study, we apply random forest clustering and density estimation for unsupervised decision. A dual assignment parameter will be used as a density estimator by combining random forest and Gaussian mixture model. Experiments were conducted using different datasets. Efficiency of using this algorithm is in capturing the underlying structure for a given set of data points. The random forest algorithm that is used in this research is robust and can discriminate between the complex features of data points among different clusters.