Learning new global relations based on an initial affinity of the database objects has shown significant improvements in similarity retrievals. Locally constrained diffusion process is one of the recent effective tools in learning the intrinsic manifold structure of a given data. Existing methods, which constrain the diffusion process locally, have problems - manual choice of optimal local neighborhood size, do not allow for intrinsic relation among the neighbors, fix initialization vector to extract dense neighbor - which negatively affect the affinity propagation. We propose a new approach, which alleviate these issues, based on some properties of a family of quadratic optimization problems related to dominant sets, a well-known graph-theoretic notion of a cluster which generalizes the concept of a maximal clique to edge-weighted graphs. In particular, we show that by properly controlling a regularization parameter which determines the structure and the scale of the underlying problem, we are in a position to extract dominant set cluster which is constrained to contain user-provided query. Experimental results on standard benchmark datasets show the effectiveness of the proposed approach.