标题： 基于核相似性度量的重叠聚类 显示英文标题

标题： Overlapping clustering based on kernel similarity metric

摘要： 生成重叠方案是聚类中的一个主要问题。 最近提出的重叠方法依赖于最优覆盖的搜索，并基于不同的度量标准，如欧几里得距离和I-散度，用于衡量观测值之间的接近程度。 在本文中，我们提出使用另一种基于核相似性度量的度量来进行重叠聚类。 我们还使用Gram矩阵估计重叠聚类的数量。 在Iris和EachMovie数据集上的实验表明了聚类数量估计的正确性，并表明基于核相似性度量的度量提高了重叠聚类的精确率、召回率和f-measure。 显示英文摘要

摘要： Producing overlapping schemes is a major issue in clustering. Recent proposed overlapping methods relies on the search of an optimal covering and are based on different metrics, such as Euclidean distance and I-Divergence, used to measure closeness between observations. In this paper, we propose the use of another measure for overlapping clustering based on a kernel similarity metric .We also estimate the number of overlapped clusters using the Gram matrix. Experiments on both Iris and EachMovie datasets show the correctness of the estimation of number of clusters and show that measure based on kernel similarity metric improves the precision, recall and f-measure in overlapping clustering.