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Dynamic knowledge graph-based incremental customer deep embedded clustering
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AI
DOI：10.1007/s41060-026-01235-1.png)
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Abstract
En 中文
Understanding dynamic customer behavior is essential for effective segmentation in evolving transactional and relational environments. Traditional clustering methods and static knowledge graphs capture only limited, fixed patterns, while deep clustering approaches often require full retraining and neglect relational and contextual information. To address these challenges, we propose IncDEC, a framework that integrates DKG with BERT-based semantic embeddings. IncDEC models evolving relationships between customers, products, and transactions, enabling rich structural and contextual representations. By incrementally updating cluster assignments and centroids in the latent space, IncDEC preserves cluster quality while adapting to new customer behaviors without retraining. Experimental results demonstrate its effectiveness, scalability, and adaptability for real-world dynamic customer segmentation.
Keywords:
Dynamic customer segmentation
Dynamic knowledge graph
BERT embeddings
Incremental deep clustering
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