For the uncertain problem that between-cluster distance influences clustering in the soft subspace clustering (SSC) process, a novel clustering technique called adaptive soft subspace clustering (ASSC) is proposed by employing both within-cluster and between-cluster information. First, a new objective function is constructed by minimizing the within-cluster compactness and maximizing the between-cluster distance based on the framework of SSC algorithm. Based on this objective function, a new way of computing clusters’ feature weights, centers and membership is then derived by using Lagrange multiplier method. The uniqueness of ASSC is that the objective function does not increase any control parameters, which can avoid the sensitivity of clustering results to the initial points of the control parameters. The properties of this algorithm are investigated and the performance is evaluated experimentally using UCI datasets. The contrastive experiment results demonstrate that the accuracy and the stability of the proposed algorithm outperform the four existing clustering algorithms, i.e., ESSC, EWKM, FWKM and CIM_QPSO_SSC.
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