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Dependability‐based cluster weighting in clustering ensemble

机译:集群集群中基于可靠性的群集加权

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After observing the ensemble success in supervised learning (such as classification), it was extended into unsupervised learning. Therefore, cluster ensemble, which merges multiple basic data partitions or clusters (called as ensemble pool) into an ordinarily better clustering solution usually named as consensus partition, emerged. Any cluster ensemble method tries to optimize a particular criterion during extracting the consensus partition out of the ensemble pool. But traditional cluster ensembles consider all the pool members with the equal importance in making the consensus partition; that is to say that each basic partition or cluster participates in the cluster ensemble algorithm equivalently. Indeed, they ignore to consider any ensemble member according to its importance. But it is obvious that some clusters with more quality deserve more emphasis and some clusters with less quality deserve less emphasis during generating consensus partition. This paper proposes (a) a metric to evaluate quality of any arbitrary cluster, (b) a mechanism to project the computed quality of a cluster into a meaningful weight value, and (c) an approach to apply the weight values of the basic clusters in the cluster ensemble process. Experimental results conducted on a number of real‐world standard datasets indicate that the proposed method outperforms the state of the art methods.
机译:在观察监督学习的合奏成功之后(如分类),它延伸到无人监督的学习。因此,群集合并,它将多个基本数据分区或集群(称为集群池)合并到通常被命名为共识分区的常规群集解决方案中。任何群集集合方法都试图在从集合池中提取共识分区期间优化特定标准。但传统的集群融合在制定共识分区时考虑所有泳池成员的同等重要;也就是说,每个基本分区或群集等效地参与集群集合算法。事实上,他们忽略了根据其重要性考虑任何合并成员。但很明显,一些具有更多质量的群集值得更加强调,在产生共识分区期间,一些具有较少质量的群集值得不那么重点。本文提出了评估任何任意簇的质量的度量,(b)将集群的计算质量投影为有意重的权重值,(c)应用基本集群的权重值的方法在集群集合过程中。在许多现实标准数据集上进行的实验结果表明该方法优于现有技术的状态。

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