The cluster analysis is dividing the individuals into clusters or groups. This job is valuable and helpful to provide facts and information about individuals. Here in this paper we proposed the cluster analysis for special model. It is the connected model for repeated measures called longitudinal and survival for individuals or patients. The statistical analysis of the connected model from the longitudinal and survival datasets becoming popular recently because it comes together in many medical applications. Here in this study, we utilized two statistical methods, cluster analysis and, connect the two models from longitudinal and survival data. It is beneficial since it gather the information from the repeated measurements, and survival responses. The shared random effect term is used joint multivariate Gaussian distribution (longitudinal) and Cox proportional hazard model (survival) for the same patients. Then, the pseudo-likelihood algorithm (clustering methodology) is performed for the joint model to distinguish the clusters or groups of patents. The application is HIV patient’s dataset with CD4+ counts responses and time to death (survival data) with some independent variables as gender and drug treatments. We conducted the clustering for S=2, and estimate the parameters from the longitudinal and survival models with and without clustering, and compare the estimations. Our results showed the generated clusters are different from each other, the estimation parameters be located around the original estimations (without clustering). It is helpful methodology to identify distinct groups or clusters from population. Finally, there is a big need for this type of application in medical elds.