标题： 通过投票的半监督聚类集成 显示英文标题

标题： Semi-supervised Clustering Ensemble by Voting

摘要： 聚类集成是无监督学习中最近的进展之一。 它的目标是结合使用不同算法或同一聚类算法对同一数据集的不同运行所得的聚类结果，这是通过共识函数来完成的，该方法的效率和准确性已在文献中的许多工作中得到证明。 在本文的第一部分，我们对文献中当前的聚类集成方法进行了比较。 所有这些方法都包括两个主要步骤：集成生成和共识函数。 在论文的第二部分，我们建议在聚类集成过程中引入监督以获得更好的聚类结果。 监督可以应用在两个地方：要么是在聚类集成生成步骤中使用半监督算法，要么是以反馈的形式由共识函数阶段使用。 此外，我们引入了一种灵活的两参数加权机制，第一个参数描述了研究的数据集与用于生成基础划分的半监督聚类算法之间的兼容性，第二个参数用于向用户提供关于这些划分的反馈。 这两个参数参与基于“重新标记和投票”的共识函数以生成最终的聚类。 显示英文摘要

摘要： Clustering ensemble is one of the most recent advances in unsupervised learning. It aims to combine the clustering results obtained using different algorithms or from different runs of the same clustering algorithm for the same data set, this is accomplished using on a consensus function, the efficiency and accuracy of this method has been proven in many works in literature. In the first part of this paper we make a comparison among current approaches to clustering ensemble in literature. All of these approaches consist of two main steps: the ensemble generation and consensus function. In the second part of the paper, we suggest engaging supervision in the clustering ensemble procedure to get more enhancements on the clustering results. Supervision can be applied in two places: either by using semi-supervised algorithms in the clustering ensemble generation step or in the form of a feedback used by the consensus function stage. Also, we introduce a flexible two parameter weighting mechanism, the first parameter describes the compatibility between the datasets under study and the semi-supervised clustering algorithms used to generate the base partitions, the second parameter is used to provide the user feedback on the these partitions. The two parameters are engaged in a "relabeling and voting" based consensus function to produce the final clustering.