A Novel Multi-Granularity Clustering Algorithm Based on Grid Partition and Fuzzy Quotient Space - Zhou, Xinran; Zhang, Qinghua; Zhao, Fan; Wang, Yutai; Yin, Longjun; Wang, Guoyin (IEEE transactions on fuzzy systems, 2025)
Clustering is a significant technique in data mining, which can uncover the hidden correlation information and obtain deeper understanding of the inherent structure of data. However, when dealing with the data with extremely uneven density and increasingly complex structure, most current clustering algorithms only obtain results at a single granular level, resulting in a unilateral understanding of the data. Therefore, a novel multi-granularity clustering algorithm based on grid partition and fuzzy quotient space (MGCGF) is proposed in this paper. Firstly, by introducing the Gaussian kernel density function to characterize the distribution characteristics of data, a grid partition method is designed to select representative points for clustering. Secondly, based on the results of grid partition, the representative points composed of the maximum density values in each dimension of the grids are used for multi-granularity clustering to improve the clustering efficiency. Finally, a multi-granularity clustering algorithm is proposed by introducing fuzzy quotient space theory with representative points as input. MGCGF can be used to uncover the hierarchical structure of the data itself and form a multi-granularity space. And the clustering results with multi-granularity can be directly provided from multi-granularity spaces without re-clustering. By comparing with the other six clustering algorithms, the feasibility is verified in terms of both efficiency and accuracy.