Unsupervised ensemble minority clustering

被引：1

作者：

Edgar Gonzàlez

Jordi Turmo

机构：

[1] Universitat Politècnica de Catalunya,TALP Research Center

[2] Google Inc.,undefined

来源：

Machine Learning | 2015年 / 98卷

关键词：

Clustering; Minority clustering; Ensemble clustering; Weak learning;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

Cluster analysis lies at the core of most unsupervised learning tasks. However, the majority of clustering algorithms depend on the all-in assumption, in which all objects belong to some cluster, and perform poorly on minority clustering tasks, in which a small fraction of signal data stands against a majority of noise.

引用

页码：217 / 268

页数：51

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