Clustering aggregation by probability accumulation
Clustering aggregation by probability accumulation
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DOI：
10.1016/j.patcog.2008.09.013
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发表时间：
2009-05
期刊：
影响因子：
--
通讯作者：
Xi Wang;Chunyu Yang;Jie Zhou
中科院分区：
文献类型：
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作者：
Xi Wang;Chunyu Yang;Jie Zhou
由于存在大量的聚类算法，聚集不同的聚类分区到一个统一的，以获得更好的结果已经成为一个重要的问题。在Fred和Jain的证据积累算法中，他们在原始划分标签上构造一个协关联矩阵，然后将最小生成树应用于该矩阵以进行组合聚类。在本文中，我们将提出一种新的聚类聚集方案，概率累积。在该算法中，相关矩阵的构造考虑了原始聚类的聚类大小。另外还提出了一种额外的预处理和后处理的替代改进算法。在合成数据集和真实的数据集上的实验结果表明，该算法的性能优于证据积累等方法。
Since a large number of clustering algorithms exist, aggregating different clustered partitions into a single consolidated one to obtain better results has become an important problem. In Fred and Jain's evidence accumulation algorithm, they construct a co-association matrix on original partition labels, and then apply minimum spanning tree to this matrix for the combined clustering. In this paper, we will propose a novel clustering aggregation scheme, probability accumulation. In this algorithm, the construction of correlation matrices takes the cluster sizes of original clusterings into consideration. An alternate improved algorithm with additional pre- and post-processing is also proposed. Experimental results on both synthetic and real data-sets show that the proposed algorithms perform better than evidence accumulation, as well as some other methods.