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Traditional outlier mining methods regard outliers from overall point, and hardly find bias data in low dimensional subspace. Constrained concept lattice, with characteristics of higher constructing efficiency , practicability and pertinency, is a new concept lattice structure. Outlier mining algorithm RCLOM based on constrained concept lattice is presented for bias data in low dimensional subspace. The intension of constrained concept lattice nodes is regarded as subspace and sparsity coefficient is computed for every intension reductions of the nodes. If sparsity coefficient of k dimensional intension reduction is less than the sparsity coefficient threshold value, k-1 dimensional intensions are judged whether it is dense subspace. If all subspaces are dense, objects contained in the intension are seen as bias data or outliers in k dimensional subspace. In the end, the algorithm is feasible and effective for mining outliers in low dimensional subspace by the example.