Journal of Engineering and Applied Sciences
ISSN: Online 1818-7803ISSN: Print 1816-949x
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.
How to cite this article:
Laith Alzubaidi, Zinah Mohsin Arkah and Reem Ibrahim Hasan. Using Random Forest Algorithm for Clustering.
DOI: https://doi.org/10.36478/jeasci.2018.9189.9193
URL: https://www.makhillpublications.co/view-article/1816-949x/jeasci.2018.9189.9193