Machine learningMachine learning
集成K均值
集成K均值通过在不同的初始化、随机种子或特征子集下多次运行K均值聚类，然后将得到的划分聚合为单一的共识分配。这种方法降低了K均值众所周知的对初始化的敏感性，并产生比单次运行更稳定、可复现的聚类。
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来源
- Strehl, A. & Ghosh, J. (2002). Cluster ensembles — a knowledge reuse framework for combining multiple partitions. Journal of Machine Learning Research, 3, 583–617. link ↗
- Monti, S., Tamayo, P., Mesirov, J. & Golub, T. (2003). Consensus clustering: a resampling-based method for class discovery and visualization of gene expression microarray data. Machine Learning, 52, 91–118. DOI: 10.1023/A:1023949509487 ↗
如何引用本页
ScholarGate. (2026, June 3). Ensemble K-means Clustering (Consensus Clustering). ScholarGate. https://scholargate.app/zh/machine-learning/ensemble-k-means
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