广西科学2025，Vol.32Issue(1)：121-131,11.DOI:10.13656/j.cnki.gxkx.20240709.001
CDKM:基于K-means聚类的因果分解方法
CDKM:A Causal Decomposition Method Based on K-means Clustering
摘要
Abstract
Redundant conditional independence tests have seriously affected the efficiency and accuracy of con-straint-based methods in causal discovery.To solve this problem,a causal decomposition method based on K-means clustering(CDKM)is proposed.CDKM divides the original causal discovery problem into multiple sub-causal discovery problems by using K-means clustering and then merges the sub-causal networks to ob-tain a complete causal network.Specifically,CDKM first uses K-means clustering to divide the original varia-ble set into k clusters and then adds two nodes with the smallest correlation distance to the current cluster from other clusters to each cluster to obtain updated k clusters.After that,it discovers causality in each clus-ter and obtain various sub-causal networks.Finally,it merges all the sub-causal networks to obtain a com-plete causal network.CDKM avoids the decomposition using high-order conditional independence tests and reduces redundant conditional independence tests.Compared with recursive constraint-based methods,CDKM can divide the original variable set into any segments.Experimental results on 8 datasets show that CDKM can greatly accelerate causal discovery,reduce time complexity,and achieve higher accuracy than baseline models.关键词
因果发现/因果分解/K-means聚类/因果网络/条件独立性测试Key words
causal discovery/causal decomposition/K-means clustering/causal network/conditional independ-ence test分类
信息技术与安全科学引用本文复制引用
韦慧娴,韦程东,陈少凡,何国源,李冶通..CDKM:基于K-means聚类的因果分解方法[J].广西科学,2025,32(1):121-131,11.基金项目
国家自然科学基金项目(11561010)资助. （11561010）
