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One prominent motivation for such work arises from the Alzheimer\u2019s Disease Neuroimaging Initiative (ADNI) cohort study, which involves multiple clinical measurements, exhibiting complex features such as diverse progression patterns, multimodality, and the presence of atypical observations. To tackle the challenges associated with modeling and clustering such grouped longitudinal data, we propose a finite mixture of multivariate contaminated normal linear mixed model (FM-MCNLMM) and its extended version, referred to as the EFM-MCNLMM, which allows the mixing weights to potentially depend on concomitant covariates. We develop alternating expectation conditional maximization algorithms to carry out maximum likelihood estimation for the two models. 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