Efficient data stream clustering via elastic sparse representation and Bayesian dictionary learning

Existing data stream clustering algorithms face two key challenges: (1) reducing resource consumption by designing algorithms that can handle continuous data streams; (2) efficiently processing large-scale data and identifying the intrinsic structures of data objects. To address these challenges, this paper introduces an efficient data stream clustering method via elastic sparse representation and Bayesian dictionary learning (ESRBDL). Firstly, we control the size of the landmark windows to ensure data object richness while using fuzzy rules to limit the number of data objects, thereby managing continuous data streams. Secondly, the elastic penalty is introduced to enhance model flexibility, balancing sparsity while improving the identification of different data characteristics. Thirdly, we apply Bayesian theory to infer the true posterior distribution from the initial dictionary distributions, effectively identifying intrinsic relationships among data objects. Finally, we use the spectral clustering algorithm to cluster data streams. Additionally, comparative experiments were conducted on five synthetic and six real datasets to benchmark the proposed method against advanced data stream clustering methods. The experimental results demonstrate the effectiveness and robustness of ESRBDL in data stream clustering.