融合局部截断距离及小簇合并的密度峰值聚类

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doi:10.6040/j.issn.1672-3961.0.2024.045

摘要： 针对密度峰值聚类算法定义的截断距离仅考虑样本全局分布,在样本分配时容易产生“多米诺骨牌”现象等问题,提出一种融合局部截断距离及小簇合并的密度峰值聚类算法。基于样本局部分布信息计算每个样本截断距离和局部密度,有利于准确获得复杂结构数据集上密度峰;根据样本决策值之间差值关系选择潜在密度峰并形成多个小簇;定义一种新的小簇间相似度,根据此相似度将小簇合并获得聚类结果,有效避免了“多米诺骨牌”现象。采用6个人工数据集和8个UCI数据集进行验证,所提算法在上述14个数据集上的标准化互信息、调整兰德系数和调整互信息平均值比5个对比算法平均提高18.15%、28.99%和20.22%,比原始密度峰值聚类算法提高30.06%,47.15%和31.90%,具有较好的聚类效果。

关键词: 聚类, 密度峰值聚类, 截断距离, 局部密度, 潜在密度峰

中图分类号:

TP391

陈素根,赵志忠. 融合局部截断距离及小簇合并的密度峰值聚类[J]. 山东大学学报 (工学版), 2025, 55(2): 58-70.

CHEN Sugen, ZHAO Zhizhong. Density peak clustering combining local truncation distance and small clusters merging[J]. Journal of Shandong University(Engineering Science), 2025, 55(2): 58-70.

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