一种基于投票策略的聚类融合算法

在分类算法和回归模型中,融合方法正得到越来越广泛的应用,但在非监督机器学习领域,由于缺乏数据集的先验知识,则不能直接用于聚类算法.提出并实现了一种基于投票策略的聚类融合算法,该算法利用k-means算法每次随机选取聚类中心而得到不同样本划分的特性,将多次运行得到的聚类结果通过投票的方式合并,从而得到最终的结果.通过一系列真实数据和合成数据集的实验证明,这种方法比单一的聚类算法能更有效地提高聚类的准确率.在此基础上,为了降低高维数据运算的复杂性,将随机划分属性子空间的方法应用到上述聚类融合算法中,实验证明,该方法同时也能够在一个属性子空间上获得好的聚类结果.

A Clustering Ensembles Algorithm Based on Voting Strategy

In the classification and regression algorithms, the ensemble method was widely used, but in the unsupervised learning, it didn't be used in the clustering algorithm directly due to lack of prior knowledge. This paper proposed a clustering ensembles algorithm based on voting strategy, it used the characteristic that the k-means algorithm selected the clustering centers randomly and found the different partitions of the sample. Then, it combined the clustering results of operating the k-means algorithm repea...