论文标题
XCLUSTERS：解释性优先群集
XClusters: Explainability-first Clustering
论文作者
论文摘要
我们研究了解释性优先聚类的问题，即解释性成为聚类的一流公民。以前的聚类方法使用决策树进行解释，但仅在群集完成后。相比之下，我们的方法是从整体上进行聚类和决策树训练，在此过程中，决策树的性能和大小也会影响聚类结果。我们假设聚类和解释的属性是不同的，尽管这不是必需的。我们观察到我们的问题是一个单调优化，其中目标函数是单调函数的差异。然后，我们提出了一种有效的分支和结合算法，用于查找最佳参数，从而导致群集失真和决策树的解释性平衡。我们的实验表明，我们的方法可以提高适合我们框架的聚类的解释性。
We study the problem of explainability-first clustering where explainability becomes a first-class citizen for clustering. Previous clustering approaches use decision trees for explanation, but only after the clustering is completed. In contrast, our approach is to perform clustering and decision tree training holistically where the decision tree's performance and size also influence the clustering results. We assume the attributes for clustering and explaining are distinct, although this is not necessary. We observe that our problem is a monotonic optimization where the objective function is a difference of monotonic functions. We then propose an efficient branch-and-bound algorithm for finding the best parameters that lead to a balance of cluster distortion and decision tree explainability. Our experiments show that our method can improve the explainability of any clustering that fits in our framework.
