Structure ensemble based on fuzzy c-means

Zhiwen Yu, Le Li, Daxing Wang, J. You, Guoqiang Han, Hantao Chen
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引用次数: 1

Abstract

Clustering ensemble is a momentous technique in machine learning and contribute much to the applications in many areas. General clustering ensemble methods pay more attention to predicting cluster labels than structures of clusters. In fact, learning cluster structures implicates sufficient information to rebuild the dataset and is competent for being the replacement of redundant predicted cluster labels. In this paper, we introduce the fuzzy theory into the structure framework and propose a newfangled double fuzzy c-means structure ensemble framework, named as FCM2SE. FCM2SE makes use of the cluster structure information instead of predicted labels to gain a representative ensemble structure. We also design two novel labeling criteria to distribute the samples to the corresponding clusters. The empirical results on synthetic datasets and UCI machine learning datasets demonstrate the effectiveness of the proposed method.
基于模糊c均值的结构集成
聚类集成是机器学习中的一项重要技术,在许多领域都有广泛的应用。一般的聚类集成方法更注重对聚类标签的预测而不是对聚类结构的预测。事实上,学习聚类结构包含足够的信息来重建数据集,并且能够替换冗余的预测聚类标签。本文将模糊理论引入到结构框架中,提出了一种新型的双模糊c均值结构集成框架,命名为FCM2SE。FCM2SE利用聚类结构信息代替预测标签来获得具有代表性的集成结构。我们还设计了两个新的标记标准来将样本分配到相应的聚类。在综合数据集和UCI机器学习数据集上的实证结果证明了该方法的有效性。
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