Lianyu Hu

Orcid: 0000-0001-7470-9395

Affiliations:
  • Dalian University of Technology, School of Software, China
  • Ningbo University, Faculty of Information Science and Engineering, China (former)


According to our database1, Lianyu Hu authored at least 22 papers between 2019 and 2025.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Dataset
Other 

Links

Online presence:

On csauthors.net:

Bibliography

2025
Interpretable categorical data clustering via hypothesis testing.
Pattern Recognit., 2025

Significance-based decision tree for interpretable categorical data clustering.
Inf. Sci., 2025

Community structure testing by counting frequent common neighbor sets.
Inf. Sci., 2025

Significance-based interpretable sequence clustering.
Inf. Sci., 2025

Interpretable sequence clustering.
Inf. Sci., 2025

2024
Node Centrality Inference via Hypothesis Testing.
Stat. Anal. Data Min., October, 2024

Central node identification via weighted kernel density estimation.
Data Min. Knowl. Discov., May, 2024

A randomized algorithm for clustering discrete sequences.
Pattern Recognit., 2024

Random subsequence forests.
Inf. Sci., 2024

Conjunction Subspaces Test for Conformal and Selective Classification.
CoRR, 2024

Interpretable Clustering: A Survey.
CoRR, 2024

Interpretable Multi-View Clustering.
CoRR, 2024

2023
Random forest clustering for discrete sequences.
Pattern Recognit. Lett., October, 2023

The statistical nature of h-index of a network node and its extensions.
J. Informetrics, August, 2023

Hamming Encoder: Mining Discriminative k-mers for Discrete Sequence Classification.
CoRR, 2023

A testing-based approach to assess the clusterability of categorical data.
CoRR, 2023

Personalized Interpretable Classification.
CoRR, 2023

2022
Significance-Based Categorical Data Clustering.
CoRR, 2022

The statistical nature of h-index of a network node.
CoRR, 2022

2021
A graph-traversal approach to identify influential nodes in a network.
Patterns, 2021

2019
Ensemble clustering based on evidence extracted from the co-association matrix.
Pattern Recognit., 2019

An Internal Validity Index Based on Density-Involved Distance.
IEEE Access, 2019


  Loading...