An integrated multi-attribute graph sequence clustering with fuzzy information granule and knowledge-guidance

Abstract

Attribute graph sequence clustering is a type of vertex clustering that meets the needs of applications, like port scale clustering, which involves vertex attribute information and historical information. However, the existing methods explore such clustering tend to focus on only one of these aspects, missing opportunity for integrated analysis of data characteristics. To address this gap, our study introduces fuzzy information granule (FIG) to include as many vertices as possible while capturing multi-faceted information with precision, thereby enabling the integration of attribute information and historical information simultaneously. Based on these granular knowledges, two algorithms are raised to realize attribute graph sequence clustering, respectively for single-attribute graph sequence (Algorithm 1) and multi-attribute graph sequence (Algorithm 2). Algorithm 1 clusters single-attribute graph sequence employing FIG based fuzzy C-means algorithm, with the objects of FIGs. Leveraging the results of single-attribute clustering, Algorithm 2 extends to multi-attribute graph sequence clustering according to knowledge-guided idea. Ultimately, an accurate clustering result for multi-attribute graph sequence is ensured by considering the clustering result of each individual attribute. Worth noting that two novel proposed clustering algorithms achieve the clustering of attribute graph sequence successfully at application level, and also facilitate that of FIGs at methodological level. After comparing the proposed multi-attribute graph sequence clustering algorithm with six other algorithms on seven datasets, Algorithm 2 wins the highest clustering accuracy (CA) and adjusted rand index (ARI) values, and the smallest FIG-Index value, particularly with CA=0.88, ARI=0.62, and FIG‐Index=0.17 on a synthetic dataset. These results demonstrate that the newly proposed algorithm significantly outperforms other algorithms in clustering accuracy and robustness.