ABSTRACT

Clustering ensemble, also referred to as consensus clustering, has emerged as a method of combining an ensemble of different clusterings to derive a final clustering that is of better quality and robust than any single clustering in the ensemble. Normally clustering ensemble algorithms in the literature combine all the clusterings without learning the ensemble. But by learning the ensemble, one can define the merit of a clustering or even a cluster in it, in forming a quality consensus. In this work, we propose a cluster-level surprisal measure to define the merit of a clustering that reflects both levels of agreement as well as disagreement among clusters. Using the proposed measure of merit, we devise a polynomial heuristics that judiciously selects a subset of clusterings from the ensemble that contribute positively in forming the consensus. We also empirically show that consensus achieved by our proposed method performs better in terms of quality compared to well-known clustering ensemble algorithms on different benchmark datasets.

摘要

聚类集成，也称为共识聚类，是一种将不同聚类的集成组合起来以得出比集成中的任何单个聚类质量更好、更稳健的最终聚类的方法。通常文献中的聚类集成算法在不学习集成的情况下组合所有聚类。但是通过学习集成，人们可以在形成质量共识时定义一个聚类甚至一个聚类的优点。在这项工作中，我们提出了一个集群级别的惊喜度量来定义集群的优点，该集群反映了集群之间的一致程度和分歧程度。使用所提出的优点度量，我们设计了一个多项式启发式算法，该算法从集合中明智地选择对形成共识有积极贡献的聚类子集。