论文标题
基于共聚类的混合型数据表的探索性分析
Co-clustering based exploratory analysis of mixed-type data tables
论文作者
论文摘要
共聚类是一类无监督的数据分析技术，可在数据表的实例和变量之间提取现有的基础依赖性结构作为均匀的块。这些技术大多数仅限于相同类型的变量。在本文中，我们提出了一种基于两步方法的混合数据共聚类方法。在第一步中，所有变量根据分析师选择的许多垃圾箱，在数值情况下以等值的频率离散化或在分类情况下保留最频繁的值进行二进制。第二步将共同群集应用于实例和二进制变量，从而导致一组实例和一组可变零件。我们将此方法应用于几个数据集，并与应用于相同数据的多个对应分析的结果进行比较。
Co-clustering is a class of unsupervised data analysis techniques that extract the existing underlying dependency structure between the instances and variables of a data table as homogeneous blocks. Most of those techniques are limited to variables of the same type. In this paper, we propose a mixed data co-clustering method based on a two-step methodology. In the first step, all the variables are binarized according to a number of bins chosen by the analyst, by equal frequency discretization in the numerical case, or keeping the most frequent values in the categorical case. The second step applies a co-clustering to the instances and the binary variables, leading to groups of instances and groups of variable parts. We apply this methodology on several data sets and compare with the results of a Multiple Correspondence Analysis applied to the same data.
