Unsupervised Decision Forest for Data Clustering and Density Estimation

An algorithm to improve performance parameter for unsupervised decision forest clustering and density estimation is presented. Specifically, a dual assignment parameter is introduced as a density estimator by combining Random Forest and Gaussian Mixture Model. The Random Forest method has been specifically applied to construct a robust affinity graph that provides information on the underlying structure of data objects used in clustering. The proposed algorithm differs from the commonly used spectral clustering methods where the computed distance metric is used to find similarities between data points. Experiments were conducted using five datasets. A comparison with six other state-of-the-art methods shows that our model is superior to existing approaches. Efficiency of the proposed model is in capturing the underlying structure for a given set of data points. The proposed method is also robust, and can discriminate between the complex features of data points among different clusters.