Active Semi-Supervision for Pairwise Constrained Clustering
Active Semi-Supervision for Pairwise Constrained Clustering
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DOI：
10.1137/1.9781611972740.31
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发表时间：
2004-06
期刊：
影响因子：
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通讯作者：
Sugato Basu;A. Banerjee;R. Mooney
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文献类型：
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作者：
Sugato Basu;A. Banerjee;R. Mooney
半监督聚类使用少量的监督数据来辅助无监督学习。一种典型的方法是在成对的示例之间指定有限数量的must-link和cannotlink约束。本文提出了一个成对约束聚类框架和一种新的方法，主动选择信息的成对约束，以获得更好的聚类性能。聚类和主动学习方法都很容易扩展到大型数据集，并且可以处理非常高维的数据。实验和理论结果证实，这种主动查询的成对约束显着提高了聚类的准确性时，给出了一个相对较小的监督。
Semi-supervised clustering uses a small amount of supervised data to aid unsupervised learning. One typical approach specifies a limited number of must-link and cannotlink constraints between pairs of examples. This paper presents a pairwise constrained clustering framework and a new method for actively selecting informative pairwise constraints to get improved clustering performance. The clustering and active learning methods are both easily scalable to large datasets, and can handle very high dimensional data. Experimental and theoretical results confirm that this active querying of pairwise constraints significantly improves the accuracy of clustering when given a relatively small amount of supervision.