投票合并方法构造中单个对象的稳定性

Norin Rahayu Shamsuddin, N. Mahat
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引用次数: 0

摘要

随机初始化的共识聚类中多个分区的构造和聚类算法的不同参数值使我们能够衡量聚类中对象(点)的稳定性。表明稳定性的过程是通过在每个分区中分配给同一集群的对对象(i,j)的共现来辅助的。本文提出了一种基于投票的方法与合并过程相结合的投票合并方法。我们在模拟和真实数据集上的实验表明,分离良好的簇和低重叠程度的簇具有更好的性能。随机初始化的共识聚类中多个分区的构造和聚类算法的不同参数值使我们能够衡量聚类中对象(点)的稳定性。表明稳定性的过程是通过在每个分区中分配给同一集群的对对象(i,j)的共现来辅助的。本文提出了一种基于投票的方法与合并过程相结合的投票合并方法。我们在模拟和真实数据集上的实验表明,分离良好的簇和低重叠程度的簇具有更好的性能。
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Stability of individual object in construction of voting-merged approach
The construction of multiple partitions in consensus clustering with random initialization and various parameters values of clustering algorithm enable us to measure the stability of objects (points) in a cluster. The procedure to indicate stability is aided by the co-occurrences of pair objects (i,j) allocated to the same cluster in each partition. In this paper, we propose a voting-merged method - a combination of voting-based method and merging process. Our experiment with simulations and real datasets shows better performance for well-separated clusters and low degree of overlapping.The construction of multiple partitions in consensus clustering with random initialization and various parameters values of clustering algorithm enable us to measure the stability of objects (points) in a cluster. The procedure to indicate stability is aided by the co-occurrences of pair objects (i,j) allocated to the same cluster in each partition. In this paper, we propose a voting-merged method - a combination of voting-based method and merging process. Our experiment with simulations and real datasets shows better performance for well-separated clusters and low degree of overlapping.
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