用标签生成聚类的逻辑解释，支持或与组合形式。
Disjunctive and Conjunctive Normal Form Explanations of Clusters Using Auxiliary Information
- 基于辅助标签，用整数规划和启发式方法生成解释
- 支持析取形式与双析取合取形式两种解释结构
- 适用于需要可解释聚类结果的研究者
本文研究如何利用聚类算法未使用的辅助信息（称为标签）来生成聚类的后验解释。重点关注两种解释形式：析取形式（由一组标签构成）和双析取合取范式（CNF）形式（由两组标签通过与操作组合而成）。采用整数线性规划（ILP）及启发式方法生成解释，并在多种数据集上进行实验，分析所得解释的洞察力。同时评估了方法的可扩展性，验证其在大规模数据上的适用性。
原文摘要 · Abstract (English)
We consider generating post-hoc explanations of clusters generated from various datasets using auxiliary information which was not used by clustering algorithms. Following terminology used in previous work, we refer to the auxiliary information as tags. Our focus is on two forms of explanations, namely disjunctive form (where the explanation for a cluster consists of a set of tags) and a two-clause conjunctive normal form (CNF) explanation (where the explanation consists of two sets of tags, combined through the AND operator). We use integer linear programming (ILP) as well as heuristic methods to generate these explanations. We experiment with a variety of datasets and discuss the insights obtained from our explanations. We also present experimental results regarding the scalability of our explanation methods.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。