Fuzzy partition based soft subspace clustering and its applications in high dimensional data

As one of the most popular clustering techniques for high dimensional data, soft subspace clustering (SSC) algorithms have been receiving a great deal of attention in recent years. Unfortunately, most existing works do not cluster high dimensional sparse data and noisy data in an effective manner. In this study, a novel soft subspace clustering algorithm called PI-SSC is proposed. By introducing a partition index (PI) into the objective function, a novel soft subspace clustering algorithm that combines the concepts of hard and fuzzy clustering is proposed. Furthermore, the robust property of PI-SSC is analyzed from the viewpoint of ε-insensitive distance. A convergence theorem for PI-SSC is also established by applying Zangwill's convergence theorem. The results of the experiment demonstrate the effectiveness of the proposed algorithm in high dimensional sparse text data and noisy texture data.