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.

中文翻译：

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聚类集成中基于可靠性的聚类加权

在观察到监督学习（例如分类）的整体成功后，将其扩展到无监督学习。因此，出现了将整体的基本数据分区或群集（称为“集成池”）合并为通常称为“共识分区”的通常更好的群集解决方案的群集集成。任何群集合奏方法都尝试在从合奏池中提取共识分区期间优化特定条件。但是传统的集群集成会考虑所有池成员在进行共识划分时具有同等的重要性。也就是说，每个基本分区或集群都等效地参与集群集成算法。实际上，他们忽略了根据其重要性来考虑任何合奏成员。但是很明显，在生成共识划分过程中，一些质量较高的集群应受到更多的重视，而质量较低的某些集群应受到较少的重视。本文提出了（a）一种评估任意群集质量的度量标准，（b）一种将群集的计算质量投影为有意义的权重值的机制，以及（c）应用基本群集的权重值的方法在集群集成过程中。在许多现实世界标准数据集上进行的实验结果表明，所提出的方法优于最新方法。（b）将计算出的群集质量投影为有意义的权重值的机制，以及（c）在群集集成过程中应用基本群集的权重值的方法。在许多现实世界标准数据集上进行的实验结果表明，所提出的方法优于最新方法。（b）将计算出的群集质量投影为有意义的权重值的机制，以及（c）在群集集成过程中应用基本群集的权重值的方法。在许多现实世界标准数据集上进行的实验结果表明，所提出的方法优于最新方法。