21 citations · 21 across the 1 of their papers we have counts for
7 papers
Interpretable Clustering: A Survey
Lianyu Hu, Mudi Jiang, Junjie Dong +2
In recent years, much of the research on clustering algorithms has primarily focused on enhancing their accuracy and efficiency, frequently at the expense of interpretability. Howe…
Adversarial Fair Multi-View Clustering
Mudi Jiang, Jiahui Zhou, Lianyu Hu +3
Cluster analysis is a fundamental problem in data mining and machine learning. In recent years, multi-view clustering has attracted increasing attention due to its ability to integ…
Two-cluster test
Xinying Liu, Lianyu Hu, Mudi Jiang +3
Cluster analysis is a fundamental research issue in statistics and machine learning. In many modern clustering methods, we need to determine whether two subsets of samples come fro…
Interpretable Clustering Ensemble
Hang Lv, Lianyu Hu, Mudi Jiang +2
Clustering ensemble has emerged as an important research topic in the field of machine learning. Although numerous methods have been proposed to improve clustering quality, most ex…
Personalized Interpretable Classification
Zengyou He, Pengju Li, Yifan Tang +3
How to interpret a data mining model has received much attention recently, because people may distrust a black-box predictive model if they do not understand how the model works. H…
Clusterability test for categorical data
Lianyu Hu, Junjie Dong, Mudi Jiang +2
The objective of clusterability evaluation is to check whether a clustering structure exists within the data set. As a crucial yet often-overlooked issue in cluster analysis, it is…