Laith  Alzubaidi, Zinah  Mohsin Arkah, Reem  Ibrahim Hasan, 
    Using Random Forest Algorithm for Clustering,
    Journal of Engineering and Applied Sciences,
    Volume 13,Issue 21,
    2018,
    Pages 9189-9193,
    ISSN 1816-949x,
    jeasci.2018.9189.9193,
    (https://makhillpublications.co/view-article.php?doi=jeasci.2018.9189.9193)
    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.
    Keywords: Random forest;clustering;Gaussian mixture;point;robust;complex