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.
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页码：217 / 268
页数：51
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