Sparse possibilistic c-means clustering with Lasso

•Possibilistic c-means (PCM) clustering by incorporating the sparsity idea with feature weights is further studied.•We propose two approaches that make the PCM clustering with the least absolute shrinkage and selection operator (Lasso), called S-PCM1 and S-PCM2.•Synthetic and real data sets are used to compare the proposed S-PCM1 and S-PCM2 with some existing algorithms.•Experimental results and comparisons demonstrate the good effectiveness and usefulness of the proposed S-PCM1 and S-PCM2 algorithms.