Year: 2022
Type: article
Abstract: better compare the studied methods in terms of robustness against outliers, we considered deterministic and random contamination mechanisms.The proposed methodology is applied to Wisconsin diagnostic breast cancer data set to diagnose malignant or benign cancer patients. Results and DiscussionThe results of the simulation study on normal and nonparanormal datasets in ideal and noisy settings, as well as the application of breast cancer data set, showed that clustering approaches based on NGMM (N GM M 0 and N GM M 1 ) are more efficient and robust in the recovery of true cluster assignments than the clustering based on GGMM (GGM M 0 and GGM M 1 ), whereas, the unconventional PMLEs (GGM M 1 and N GM M 1 ) are more efficient in estimating the elements of precision matrices than the conventional PMLEs (GGM M 0 and N GM M 0 ). ConclusionThe performance of clustering methods depends on the choice of penalty function and model selection, such that the combination of the nonparanormal graphical mixture model and the penalty term depending on the mixing proportions (N GM M 1 ) is more accurate than Gaussian ones in terms of cluster reconstruction and parameters estimation.
Source: Journal of Statistical Sciences
DOI: 10.52547/jss.16.1.63
Authors: Farzad Eskandari, Hamid Haji Aghabozorgi
Institution: Allameh Tabataba'i University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4290803287