Weighted Subspace Fuzzy Clustering with Adaptive Projection

Abstract

Available subspace clustering methods often contain two stages, finding low-dimensional subspaces of data and then conducting clustering in the subspaces. Therefore, how to find the subspaces that better represent the original data becomes a research challenge. However, most of the reported methods are based on the premise that the contributions of different features are equal, which may not be ideal for real scenarios, i.e., the contributions of the important features may be overwhelmed by a large amount of redundant features. In this study, a weighted subspace fuzzy clustering (WSFC) model with a locality preservation mechanism is presented, which can adaptively capture the importance of different features, achieve an optimal lower-dimensional subspace, and perform fuzzy clustering simultaneously. Since each feature can be well quantified in terms of its importance, the proposed model exhibits the sparsity and robustness of fuzzy clustering. The intrinsic geometrical structures of data can also be preserved while enhancing the interpretability of clustering tasks. Extensive experimental results show that WSFC can allocate appropriate weights to different features according to data distributions and clustering tasks and achieve superior performance compared to other clustering models on real-world datasets.