TY  - JOUR
T1  - Using Random Forest Algorithm for Clustering
AU - Alzubaidi, Laith AU - Mohsin Arkah, Zinah AU - Ibrahim Hasan, Reem 
JO  - Journal of Engineering and Applied Sciences
VL  - 13
IS  - 21
SP  - 9189
EP  - 9193
PY  - 2018
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2018.9189.9193
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2018.9189.9193
KW  - Random forest
KW  -clustering
KW  -Gaussian mixture
KW  -point
KW  -robust
KW  -complex
AB  - 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.
ER  -