Machine learning
BIRCH — 基于层次结构的平衡迭代规约和聚类
BIRCH（Balanced Iterative Reducing and Clustering using Hierarchies）是由Zhang、Ramakrishnan和Livny于1996年提出的一种可扩展的增量聚类算法。它旨在通过将数据压缩成紧凑的内存摘要结构，即CF树（Clustering Feature tree），然后在应用任何标准聚类过程之前，对非常大的数据集（可能大于可用内存）进行单次扫描聚类。
阅读完整方法
仅限会员
登录使用免费账户登录即可阅读本节。
Method map
The neighbourhood of related methods — select a node to explore.
来源
- Zhang, T., Ramakrishnan, R., & Livny, M. (1996). BIRCH: An efficient data clustering method for very large databases. Proceedings of the 1996 ACM SIGMOD International Conference on Management of Data, 25(2), 103–114. DOI: 10.1145/233269.233324 ↗
- Han, J., Kamber, M., & Pei, J. (2011). Data Mining: Concepts and Techniques (3rd ed., Ch. 10). Morgan Kaufmann. ISBN: 978-0-12-381479-1
如何引用本页
ScholarGate. (2026, June 3). Balanced Iterative Reducing and Clustering using Hierarchies. ScholarGate. https://scholargate.app/zh/machine-learning/birch
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
Compare side by side →