点击次数：

所属单位：计算机学院

教研室：智能科学与计算

发表刊物：Information Fusion

刊物所在地：荷兰

项目来源：国家自然科学青年基金

关键字：Clustering, Natural neighbor, Multi-granularity cognitive learning, Granular-ball Computing

摘要：The real-world dataset exhibits diversity, incorporating instances with complex shapes and significant differences in density hierarchy, potentially disrupted by noise. However, most clustering algorithms typically rely
on single-granularity fusion, requiring the pre-setting of global parameters for the entire dataset. Nevertheless,
these global parameters may not adequately adapt to clusters with varying hierarchies or shapes, consequently
reducing the clustering effectiveness. Therefore, we propose an adaptive density clustering approach with
multi-granularity fusion. This approach characte

论文类型：期刊论文

学科门类：工学

一级学科：电子与信息类

文献类型：J

ISSN号：1566-2535

是否译文：否

发表时间：2024-02-07

收录刊物：SCI