A robust density-based clustering algorithm for multi-manifold structure

In real-world pattern recognition tasks, the data with multiple manifolds structure is ubiquitous and unpredictable. Performing an effective clustering on such data is a challenging problem. In particular, it is not obvious how to design a similarity measure for multiple manifolds. In this paper, we address this problem proposing a new manifold distance measure, which can better capture both local and global spatial manifold information. We define a new way of local density estimation accounting for the density characteristic. It represents local density more accurately. Meanwhile, it is less sensitive to the parameter settings. Besides, in order to select the cluster centers automatically, a two-phase exemplar determination method is proposed. The experiments on several synthetic and real-world datasets show that the proposed algorithm has higher clustering effectiveness and better robustness for data with varying density, multi-scale and noise overlap characteristics.