Cluster Ensembles
Cluster Ensembles
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DOI：
10.1002/9781118445112.stat08170
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发表时间：
2019-08
期刊：
影响因子：
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通讯作者：
A. Acharya;Joydeep Ghosh
中科院分区：
文献类型：
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作者：
A. Acharya;Joydeep Ghosh
集群集成联合收割机将一组对象的多个集群组合成一个统一的集群，通常称为共识解决方案。与单一聚类方法相比，共识聚类可以用于生成更健壮和稳定的聚类结果，在隐私或共享约束下执行分布式计算，或重用现有知识。本文介绍了各种算法，已提出解决集群集成问题，组织它们在概念类别，带出共同的线程和经验教训，而在同一时间突出的独特功能的个别方法。
Cluster ensembles combine multiple clusterings of a set of objects into a single consolidated clustering, often referred to as theconsensussolution. Consensus clustering can be used to generate more robust and stable clustering results compared to a single clustering approach, perform distributed computing under privacy or sharing constraints, or reuse existing knowledge. This article describes a variety of algorithms that have been proposed to address the cluster ensemble problem, organizing them in conceptual categories that bring out the common threads and lessons learned, while at the same time highlighting the unique features of individual approaches.