78 citations · 97 across the 12 of their papers we have counts for
11 papers · 1 filter
Imputation Meets Clustering: Exploiting Latent Subgroup Structure for Missing Data Recovery
Chuyao Zhang, E Li, Taochen Chen +5
Missing data is prevalent in practical applications, making effective imputation an essential preprocessing step for downstream analysis. Real-world datasets often exhibit complex…
Robust Categorical Data Clustering Guided by Multi-Granular Competitive Learning
Shenghong Cai, Yiqun Zhang, Xiaopeng Luo +3
Data set composed of categorical features is very common in big data analysis tasks. Since categorical features are usually with a limited number of qualitative possible values, th…
Evo-TFS: Evolutionary Time-Frequency Domain-Based Synthetic Minority Oversampling Approach to Imbalanced Time Series Classification
Wenbin Pei, Ruohao Dai, Bing Xue +3
Time series classification is a fundamental machine learning task with broad real-world applications. Although many deep learning methods have proven effective in learning time-ser…
Bridging the Semantic Gap for Categorical Data Clustering via Large Language Models
Zihua Yang, Xin Liao, Yiqun Zhang +1
Qualitative data are widespread in domains such as healthcare, marketing, and bioinformatics, where clustering offers a fundamental tool for pattern discovery. A core difficulty of…
CADM: Cluster-customized Adaptive Distance Metric for Categorical Data Clustering
Taixi Chen, Yiu-ming Cheung, Yiqun Zhang
An appropriate distance metric is crucial for categorical data clustering, as the distance between categorical data cannot be directly calculated. However, the distances between at…
MEET-Sepsis: Multi-Endogenous-View Enhanced Time-Series Representation Learning for Early Sepsis Prediction
Zexi Tan, Tao Xie, Binbin Sun +3
Sepsis is a life-threatening infectious syndrome associated with high mortality in intensive care units (ICUs). Early and accurate sepsis prediction (SP) is critical for timely int…
EvoSampling: A Granular Ball-based Evolutionary Hybrid Sampling with Knowledge Transfer for Imbalanced Learning
Wenbin Pei, Ruohao Dai, Bing Xue +4
Class imbalance would lead to biased classifiers that favor the majority class and disadvantage the minority class. Unfortunately, from a practical perspective, the minority class…
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…
Learning Self-Growth Maps for Fast and Accurate Imbalanced Streaming Data Clustering
Yiqun Zhang, Sen Feng, Pengkai Wang +5
Streaming data clustering is a popular research topic in data mining and machine learning. Since streaming data is usually analyzed in data chunks, it is more susceptible to encoun…
Enhancing the Performance of Neural Networks Through Causal Discovery and Integration of Domain Knowledge
Xiaoge Zhang, Xiao-Lin Wang, Fenglei Fan +2
In this paper, we develop a generic methodology to encode hierarchical causality structure among observed variables into a neural network in order to improve its predictive perform…
Competitive and Penalized Clustering Auto-encoder
Zihao Wang, Yiuming Cheung
The paper has been withdrawn since more effective experiments should be completed. Auto-encoders (AE) has been widely applied in different fields of machine learning. However, as a…