18 papers
Efficient Mining of Low-Utility Sequential Patterns
Jian Zhu, Zhidong Lin, Wensheng Gan +1
Discovering valuable insights from rich data is a crucial task for exploratory data analysis. Sequential pattern mining (SPM) has found widespread applications across various domai…
Domain-Aware Hierarchical Contrastive Learning for Semi-Supervised Generalization Fault Diagnosis
Junyu Ren, Wensheng Gan, Philip S Yu
Fault diagnosis under unseen operating conditions remains highly challenging when labeled data are scarce. Semi-supervised domain generalization fault diagnosis (SSDGFD) provides a…
High-utility Sequential Rule Mining Utilizing Segmentation Guided by Confidence
Chunkai Zhang, Jiarui Deng, Maohua Lyu +2
Within the domain of data mining, one critical objective is the discovery of sequential rules with high utility. The goal is to discover sequential rules that exhibit both high uti…
Discovering High-utility Sequential Rules with Increasing Utility Ratio
Zhenqiang Ye, Wensheng Gan, Gengsen Huang +2
Utility-driven mining is an essential task in data science, as it can provide deeper insight into the real world. High-utility sequential rule mining (HUSRM) aims at discovering se…
Enhancing Temporal Awareness in LLMs for Temporal Point Processes
Lili Chen, Wensheng Gan, Shuang Liang +1
Temporal point processes (TPPs) are crucial for analyzing events over time and are widely used in fields such as finance, healthcare, and social systems. These processes are partic…
Graph Attention-based Adaptive Transfer Learning for Link Prediction
Huashen Lu, Wensheng Gan, Guoting Chen +2
Graph neural networks (GNNs) have brought revolutionary advancements to the field of link prediction (LP), providing powerful tools for mining potential relationships in graphs. Ho…