26 citations · 41 across the 7 of their papers we have counts for
7 papers
How to Ask the AI: A User Perspective Survey for Large Language Model Prompting
Yiqun Zhang, Yunfan Zhang, Mingjie Zhao +2
AI tools like ChatGPT and DeepSeek, powered by Large Language Models (LLMs), allow users to obtain instant and effective content responses simply by typing requests, such as ``plan…
Beyond Statistical Co-occurrence: Unlocking Intrinsic Semantics for Tabular Data Clustering
Mingjie Zhao, Yunfan Zhang, Yiqun Zhang +1
Deep Clustering (DC) has emerged as a powerful tool for tabular data analysis in real-world domains like finance and healthcare. However, most existing methods rely on data-level s…
Learning Unbiased Cluster Descriptors for Interpretable Imbalanced Concept Drift Detection
Yiqun Zhang, Zhanpei Huang, Mingjie Zhao +5
Unlabeled streaming data are usually collected to describe dynamic systems, where concept drift detection is a vital prerequisite to understanding the evolution of systems. However…
Learning Unified Distance Metric for Heterogeneous Attribute Data Clustering
Yiqun Zhang, Mingjie Zhao, Yizhou Chen +2
Datasets composed of numerical and categorical attributes (also called mixed data hereinafter) are common in real clustering tasks. Differing from numerical attributes that indicat…
Learning Order Forest for Qualitative-Attribute Data Clustering
Mingjie Zhao, Sen Feng, Yiqun Zhang +3
Clustering is a fundamental approach to understanding data patterns, wherein the intuitive Euclidean distance space is commonly adopted. However, this is not the case for implicit…
Break the Tie: Learning Cluster-Customized Category Relationships for Categorical Data Clustering
Mingjie Zhao, Zhanpei Huang, Yang Lu +4
Categorical attributes with qualitative values are ubiquitous in cluster analysis of real datasets. Unlike the Euclidean distance of numerical attributes, the categorical attribute…
Categorical Data Clustering via Value Order Estimated Distance Metric Learning
Yiqun Zhang, Mingjie Zhao, Hong Jia +3
Clustering is a popular machine learning technique for data mining that can process and analyze datasets to automatically reveal sample distribution patterns. Since the ubiquitous…