Support structure representation learning for sequential data clustering. (February 2022) - Wang, Xiumei; Guo, Dingning; Cheng, Peitao
Highlights: A structure representation learning method for sequential data clustering is presented. The proposed method extracts both sequential and spatial information. The structure-preserving property and the connectivity of the method are proved. The proposed method achieves better performance than the state-of-the-art methods. Abstract: Sequential data clustering is a challenging task in data mining (e.g., motion recognition and video segmentation). For good performance in dealing with complex local correlation and high-dimensional structure of sequential data, representation based methods have become one of the hot topics for sequential data clustering, in which subspace clustering is a representative tool. Subspace clustering methods divide the sequence into disjoint segments according to a locally continuous and connected representation of raw data. Although the subspace clustering methods maintain the successive property of sequential data well, there exist redundant connections in the intersection of two subsequences, which will destroy the integrity of a cluster and easily cause the chained partition of the sequence. So it is necessary to learn a more specific structure representation of a sequence to preserves both sequential information and efficient connections. Besides, the representation that conducive to clustering should have sparsity and connectivity under some assumptions. To this end, we propose a novel method to learn the support structure representation of sequence, which can extract sufficient information about instances and get the compact structure of sequential data. Furthermore, a new subspace clustering method is proposed based on the representation based method. Theoretical analysis and experimental results show the effectiveness of the proposed method.