Abstract: Most existing Gaussian mixture model (GMM) with hard equivalence constraints cannot solve the problem of violating pairwise constraints, and the GMM with soft equivalence constraints lacks of closed estimation forms of model parameters. This paper presents a generative clustering analysis algorithm with constraints regularization (GCACR), in which the constraints consistent assumption is integrating into GMM. To penalize the constraint violation, the penalized likelihood function is designed. This makes the posterior probability of the pairwise data points similar or dissimilar according to the data points coming from positive constraints or negative constraints. Meanwhile, the computational complexity of model parameter estimation is reduced for the closed estimation forms of model parameters being provided. Experimental results on realworld datasets show that the proposed algorithm can improve the clustering performance, and can better adapt to dealing with the noise pairwise constraints compared with the stateoftheart generative clustering algorithms.