Consensus Clustering Based on a New Probabilistic Rand Index with Application to Subtopic Retrieval
Consensus Clustering Based on a New Probabilistic Rand Index with Application to Subtopic Retrieval
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DOI：
10.1109/tpami.2012.80
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发表时间：
2012-12-01
影响因子：
23.6
通讯作者：
Romano, Giovanni
中科院分区：
文献类型：
--
作者：
Carpineto, Claudio;Romano, Giovanni
我们引入了一种用于度量两个划分之间相似性的概率版本的Rand Index(RI)，称为概率Rand Index(PRI)，其中对象对级别上的一致和不一致根据它们偶然出现的概率进行加权。然后，我们将共识聚类归结为一个目标分区和一组给定分区之间的PRI值的优化问题，并用一个简单而高效的随机优化算法进行了实验。通过一系列应用展示了与输入分区和现有相关方法相比的显著性能提升，包括使用共识聚类来改进子主题检索。
We introduce a probabilistic version of the well-known Rand Index (RI) for measuring the similarity between two partitions, called Probabilistic Rand Index (PRI), in which agreements and disagreements at the object-pair level are weighted according to the probability of their occurring by chance. We then cast consensus clustering as an optimization problem of the PRI value between a target partition and a set of given partitions, experimenting with a simple and very efficient stochastic optimization algorithm. Remarkable performance gains over input partitions as well as over existing related methods are demonstrated through a range of applications, including a new use of consensus clustering to improve subtopic retrieval.