A Shared Natural Neighbors Based-Hierarchical Clustering Algorithm for Discovering Arbitrary-Shaped Clusters

In clustering algorithms, the selection of neighbors significantly affects the quality of the final clustering results. While various neighbor relationships exist, such as K-nearest neighbors, natural neighbors, and shared neighbors, most neighbor relationships can only handle single structural relationships, and the identification accuracy is low for datasets with multiple structures. In life, people's first instinct for complex things is to divide them into multiple parts to complete. Partitioning the dataset into more sub-graphs is a good idea approach to identifying complex structures. Taking inspiration from this, we propose a novel neighbor method: Shared Natural Neighbors (SNaN). To demonstrate the superiority of this neighbor method, we propose a shared natural neighbors-based hierarchical clustering algorithm for discovering arbitrary-shaped clusters (HC-SNaN). Our algorithm excels in identifying both spherical clusters and manifold clusters. Tested on synthetic datasets and real-world datasets, HC-SNaN demonstrates significant advantages over existing clustering algorithms, particularly when dealing with datasets containing arbitrary shapes.