In this paper, we introduce a novel algorithm that unifies manifold embedding and clustering (UEC) which efficiently predicts clustering assignments of the high dimensional data points in a new embedding space. The algorithm is based on a bi-objective optimisation problem combining embedding and clustering loss functions. Such original formulation will allow to simultaneously preserve the original structure of the data in the embedding space and produce better clustering assignments. The experimental results using a number of real-world datasets show that UEC is competitive with the state-of-art clustering methods.