Hashing methods map similar data to binary hashcodes with smaller hamming distance, and it has received a broad attention due to its low storage cost and fast retrieval speed. However, the existing limitations make the present algorithms difficult to deal with large-scale datasets: (1) discrete constraints are involved in the learning of the hash function, (2) pairwise or triplet similarity is adopted to generate efficient hashcodes, resulting both time and space complexity are greater than O(n2). To address these issues, we propose a novel discrete supervised hash learning framework which can be scalable to large-scale datasets. First, the learning procedure is decomposed into a binary classifier learning scheme and hashcodes learning scheme. Second, we adopt the Asymmetric Low-rank Matrix Factorization and propose the Fast Clustering-based Batch Coordinate Descent method, such that the time and space complexity is reduced to O(n). The proposed framework also provides a flexible paradigm to incorporate with arbitrary hash function, including deep neural networks. Experiments on large-scale datasets demonstrate that the proposed method is superior or comparable with state-of-the-art hashing algorithms.