Journal of Data Mining and Knowledge Engineering ISSN: 3139-5341 (Online)

Abstract

Traditional data mining techniques mostly rely on pairwise relationships represented by graphs or relational tables. However, many real-world systems naturally contain interactions among more than two entities simultaneously, such as co-authorship networks, biological pathways, and group communications. These complex multi-entity relations can be better modeled using hypergraphs and higher-order structures. Hypergraph and higher-order data mining has emerged as an important research direction to capture group level dependencies, collective patterns, and multi-way associations that cannot be discovered using conventional graph-based methods. This paper presents a comprehensive review of hypergraph and higher-order data mining. We first introduce the theoretical foundations of hypergraphs and their properties. Then we discuss hypergraph representation learning, clustering, classification, and community detection methods. We also examine higher-order network mining including simplicial complexes and tensor-based approaches. Applications in social networks, recommender systems, bioinformatics, and knowledge graphs are analyzed. A comparative analysis of algorithms and challenges is also presented. Finally, open research directions such as scalability, interpretability, and dynamic hypergraphs are discussed. The study shows that hypergraph-based mining provides richer structural understanding compared to traditional graph mining and is essential for modeling modern complex data systems.

Keywords: Hypergraph mining, higher-order networks, multi-relational data, hypergraph clustering, tensor mining, group interactions