@article{MAKHILLJEAS2018132117092,
    title = {Using Random Forest Algorithm for Clustering},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {13},
    number = {21},
    pages = {9189-9193},
    year = {2018},
    issn = {1816-949x},
    doi = {jeasci.2018.9189.9193},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2018.9189.9193},
    author = {Laith,Zinah and},
    keywords = {Random forest,clustering,Gaussian mixture,point,robust,complex},
    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.}
    }