Data stream clustering via fuzzy similarity and diffusion-enhanced contextual affinity

Data stream clustering provides an effective method for recognizing underlying patterns in potentially unbounded sequences of data objects. Existing data stream clustering methods primarily encounter two key issues: (1) the inadequate evaluation of relationships between data objects within fixed-size landmark windows, leading to degraded clustering quality; and (2) the absence of efficient mechanisms for transferring useful knowledge from previous windows to the current window, weakening the model’s adaptability to data stream evolution. To address these issues, a data stream clustering method based on axiomatic fuzzy set theory via a diffusion process is first proposed. First, the proposed method employs axiomatic fuzzy set theory to measure the relationships between data objects within the window, capturing similarity information to more accurately reveal the underlying data distribution. Second, an efficient diffusion process enhances pairwise affinities through contextual propagation, which significantly improves connectivity within clusters. Finally, the learned affinity matrix is applied to spectral clustering for data stream clustering. Over time, we update the dynamic set according to the distance between data objects and cluster centers. This dynamic set retains representative data objects and effectively transfers previously learned knowledge to the current landmark window. Experimental results on four datasets and seven algorithms demonstrate the effectiveness and robustness of the proposed method.