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Multi-Optimisation Consensus Clustering

机译:多优化共识聚类

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摘要

Ensemble Clustering has been developed to provide an alternative way of obtaining more stable and accurate clustering results. It aims to avoid the biases of individual clustering algorithms. However, it is still a challenge to develop an efficient and robust method for Ensemble Clustering. Based on an existing ensemble clustering method, Consensus Clustering (CC), this paper introduces an advanced Consensus Clustering algorithm called Multi-Optimisation Consensus Clustering (MOCC), which utilises an optimised Agreement Separation criterion and a Multi-Optimisation framework to improve the performance of CC. Fifteen different data sets are used for evaluating the performance of MOCC. The results reveal that MOCC can generate more accurate clustering results than the original CC algorithm.
机译:已经开发了合奏聚类,以提供更稳定和准确的聚类结果的替代方法。它旨在避免单个聚类算法的偏差。但是,为合奏聚类开发一种有效和鲁棒方法仍然是一项挑战。基于现有的集群群集方法,本文介绍了称为多优化共识聚类(MOCC)的高级共识群集算法,该算法利用优化的协议分离标准和多优化框架来提高性能CC。十五个不同的数据集用于评估MOCC的性能。结果表明,MOCC可以产生比原始CC算法更准确的聚类结果。

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