A constrained principal component analysis, which aims at a simultaneous clustering of objects and a partitioning of variables is proposed. The new methodology allows to identify components with maximum variance, each one a linear combination of a subset of variables. All the subsets form a partition of variables. Simultaneously, a partition of objects is also computed maximizing the between cluster variance. The methodology is formulated in a semi-parametric least-squares framework as a quadratic mixed continuous and integer problem. An alternating leastsquares algorithm is proposed to solve the clustering and disjoint PCA. Two applications are given to show the features of the methodology.