Visual exploration of multi-dimensional data via rule-based sample embedding  

作　　者：Tong Zhang Jie Li Chao Xu 

机构地区：[1]College of Intelligence and Computing,Tianjin University,Tianjin,China

出　　处：《Visual Informatics》2024年第3期53-56,共4页可视信息学（英文）

基　　金：supported by NSFC project(62372321).

摘　　要：We propose an approach to learning sample embedding for analyzing multi-dimensional datasets.The basic idea is to extract rules from the given dataset and learn the embedding for each sample based on the rules it satisfies.The approach can filter out pattern-irrelevant attributes,leading to significant visual structures of samples satisfying the same rules in the projection.In addition,analysts can understand a visual structure based on the rules that the involved samples satisfy,which improves the projection’s pattern interpretability.Our research involves two methods for achieving and applying the approach.First,we give a method to learn rule-based embedding for each sample.Second,we integrate the method into a system to achieve an analytical workflow.Cases on real-world dataset and quantitative experiment results show the usability and effectiveness of our approach.

关 键 词：Tabular data Multi-dimensional exploration Embedding projection RULE Visual analytics 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]