A Survey: Clustering Ensembles Techniques
A Survey: Clustering Ensembles Techniques
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发表时间：
2009-02
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通讯作者：
R. Ghaemi;M. N. Sulaiman;H. Ibrahim;N. Mustapha
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作者：
R. Ghaemi;M. N. Sulaiman;H. Ibrahim;N. Mustapha
聚类集成将不同聚类算法生成的多个分区组合为单个聚类解决方案。聚类集成已经成为提高无监督分类方案鲁棒性、稳定性和准确性的重要方法。到目前为止，已经做了许多贡献来找到共识聚类。聚类集成的主要问题之一是一致性函数。本文首先介绍了聚类集成、多分区的表示及其挑战，并给出了组合算法的分类。其次，我们描述了聚类集成中的共识函数，包括超图划分、投票方法、互信息、基于协关联的函数和有限混合模型，然后解释了它们的优缺点和计算复杂度。最后，我们比较了聚类集成算法在不同数据集上的计算复杂度、鲁棒性、简单性和准确性。
The clustering ensembles combine multiple partitions generated by different clustering algorithms into a single clustering solution. Clustering ensembles have emerged as a prominent method for improving robustness, stability and accuracy of unsupervised classification solutions. So far, many contributions have been done to find consensus clustering. One of the major problems in clustering ensembles is the consensus function. In this paper, firstly, we introduce clustering ensembles, representation of multiple partitions, its challenges and present taxonomy of combination algorithms. Secondly, we describe consensus functions in clustering ensembles including Hypergraph partitioning, Voting approach, Mutual information, Co-association based functions and Finite mixture model, and next explain their advantages, disadvantages and computational complexity. Finally, we compare the characteristics of clustering ensembles algorithms such as computational complexity, robustness, simplicity and accuracy on different datasets in previous techniques.