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Sparse subspace clustering (SSC) has achieved state-of-the-art clustering quality by performing spectral clustering over an ℓ1-norm based similarity graph. However, SSC is a transductive method, i.e. it cannot handle out-of-sample data that is not used to construct the graph. For each new datum, SSC requires solving n optimisation problems in O(n) variables, where n is the number of data points. Therefore, it is inefficient to apply SSC in fast online clustering and scalable grouping. An inductive spectral clustering algorithm called inductive SSC (iSSC) is proposed, which makes SSC feasible to cluster out-of-sample data. iSSC adopts the assumption that high-dimensional data actually lie on the low-dimensional manifold such that out-of-sample data could be grouped in the embedding space learned from in-sample data. Experimental results show that iSSC is promising in clustering out-of-sample data.