论文标题
基于Barycentric编码的混合型数据的分层聚类
Hierarchical clustering of mixed-type data based on barycentric coding
论文作者
论文摘要
混合型数据集的聚类可能是一项特别具有挑战性的任务，因为它需要考虑具有不同测量级别的变量之间的关联，即标称，序数和/或间隔。在某些情况下，层次聚类被认为是一种合适的方法，因为它对数据做出了很少的假设，并且可以轻松地将其解决方案可视化。由于大多数层次聚类方法都假定变量是在相同规模上测量的，因此聚类混合型数据的一个简单策略是在聚类之前均质化变量。这意味着要么重新编码连续变量为分类变量，反之亦然。但是，连续变量的典型离散是意味着信息丢失。在这项工作中，提出了用于混合型数据的聚集层次聚类方法，该方法依赖于连续变量的barycentric编码。提出的方法最大程度地减少了信息丢失，并且与对应分析的框架兼容。该方法的实用性在真实和模拟数据上证明。
Clustering of mixed-type datasets can be a particularly challenging task as it requires taking into account the associations between variables with different level of measurement, i.e., nominal, ordinal and/or interval. In some cases, hierarchical clustering is considered a suitable approach, as it makes few assumptions about the data and its solution can be easily visualized. Since most hierarchical clustering approaches assume variables are measured on the same scale, a simple strategy for clustering mixed-type data is to homogenize the variables before clustering. This would mean either recoding the continuous variables as categorical ones or vice versa. However, typical discretization of continuous variables implies loss of information. In this work, an agglomerative hierarchical clustering approach for mixed-type data is proposed, which relies on a barycentric coding of continuous variables. The proposed approach minimizes information loss and is compatible with the framework of correspondence analysis. The utility of the method is demonstrated on real and simulated data.
