<p>Neighborhood learning (NL) constructs both local and global structures within the cluster space, significantly enhancing clustering performance. To determine the neighbors of samples, neighborhood-based clustering methods typically require searching the entire sample space. However, this exhaustive search substantially increases the clustering time, leading to additional computational overhead. Granular cabin (GC) offers an efficient strategy for generating neighborhood information granule (NIG). With GC, the nearest neighbors of samples can be rapidly identified without the need to search the entire sample space. Inspired by this concept, we integrate GC into neighborhood-based clustering and propose two types of GC. Specifically, these two types are applied to Density Peaks Clustering (DPC) and Spectral Clustering (SC), resulting in the methods GC-DPC, GC2-DPC, GC-SC, and GC2-SC. The proposed methods first generate NIG using GC and then compute nearest neighbors within NIG. The nearest neighbors obtained through GC are subsequently used to calculate the local density in DPC and the adjacency matrix in SC. By reducing the computation of nearest neighbors, GC improves the efficiency of clustering methods. We compare the granular cabin-embedded clustering methods with popular clustering methods, including K-means, FCM, Rough k-means, DPC, SC, and KNN-DPC. The experiments are verified on both synthetic and real datasets. Experimental results demonstrate that the granular cabin-based clustering methods enhance the clustering speed without sacrificing performance. Furthermore, granular cabin-embedded accelerator for clustering methods is non-intrusive, preserving the structure of the original clustering methods.</p>