Lianyu Hu

Orcid: 0000-0001-7470-9395

Affiliations:

Henan University of Technology, Zhengzhou, China
Dalian University of Technology, School of Software, China (PhD 2025)
Ningbo University, Faculty of Information Science and Engineering, China (former)

According to our database¹, Lianyu Hu authored at least 33 papers between 2019 and 2026.

Collaborative distances:

Dijkstra number² of four.
Erdős number³ of four.

Timeline

Legend:

Book In proceedings Article PhD thesis Dataset Other

Bibliography

2026

Personalized interpretable classification.

[BibT_eX]

[DOI]

Knowl. Inf. Syst., December, 2026

Interpretable Clustering: A Survey.

[BibT_eX]

[DOI]

ACM Comput. Surv., June, 2026

Interpretable Sequence Classification via Decision Set.

[BibT_eX]

[DOI]

IEEE Trans. Knowl. Data Eng., May, 2026

Clustering With Multiview Explanations.

[BibT_eX]

[DOI]

IEEE Trans. Knowl. Data Eng., May, 2026

Clustering Validation via Sample Pair Co-Cluster Testing.

[BibT_eX]

[DOI]

IEEE Trans. Knowl. Data Eng., February, 2026

Robust density clustering based on density peak regions and K-nearest neighbors.

[BibT_eX]

[DOI]

Pattern Recognit., 2026

Adversarial fair multi-view clustering.

[BibT_eX]

[DOI]

Pattern Recognit., 2026

2025

Hamming encoder: mining discriminative k-mers for discrete sequence classification.

[BibT_eX]

[DOI]

Data Min. Knowl. Discov., July, 2025

Two-cluster test.

[BibT_eX]

[DOI]

CoRR, July, 2025

Interpretable Clustering Ensemble.

[BibT_eX]

[DOI]

CoRR, June, 2025

Clusterability test for categorical data.

[BibT_eX]

[DOI]

Knowl. Inf. Syst., May, 2025

Clustering Categorical Data via Multiple Hypothesis Testing.

[BibT_eX]

[DOI]

ACM Trans. Knowl. Discov. Data, 2025

Interpretable multi-view clustering.

[BibT_eX]

[DOI]

Pattern Recognit., 2025

Interpretable categorical data clustering via hypothesis testing.

[BibT_eX]

[DOI]

Pattern Recognit., 2025

Significance-based decision tree for interpretable categorical data clustering.

[BibT_eX]

[DOI]

Inf. Sci., 2025

Community structure testing by counting frequent common neighbor sets.

[BibT_eX]

[DOI]

Inf. Sci., 2025

Conjunction subspaces test for conformal and selective classification.

[BibT_eX]

[DOI]

Inf. Sci., 2025

Significance-based interpretable sequence clustering.

[BibT_eX]

[DOI]

Inf. Sci., 2025

Interpretable sequence clustering.

[BibT_eX]

[DOI]

Inf. Sci., 2025

Statistical significance of cluster membership for categorical data.

[BibT_eX]

[DOI]

Eng. Appl. Artif. Intell., 2025

2024

Node Centrality Inference via Hypothesis Testing.

[BibT_eX]

[DOI]

Stat. Anal. Data Min., October, 2024

Central node identification via weighted kernel density estimation.

[BibT_eX]

[DOI]

Data Min. Knowl. Discov., May, 2024

A randomized algorithm for clustering discrete sequences.

[BibT_eX]

[DOI]

Pattern Recognit., 2024

Random subsequence forests.

[BibT_eX]

[DOI]

Inf. Sci., 2024

2023

Random forest clustering for discrete sequences.

[BibT_eX]

[DOI]

Pattern Recognit. Lett., October, 2023

The statistical nature of h-index of a network node and its extensions.

[BibT_eX]

[DOI]

J. Informetrics, August, 2023

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

[BibT_eX]

[DOI]

CoRR, 2023

Personalized Interpretable Classification.

[BibT_eX]

[DOI]

CoRR, 2023

2022

Significance-Based Categorical Data Clustering.

[BibT_eX]

[DOI]

CoRR, 2022

The statistical nature of h-index of a network node.

[BibT_eX]

[DOI]

CoRR, 2022

2021

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

[BibT_eX]

[DOI]

Patterns, 2021

2019

Ensemble clustering based on evidence extracted from the co-association matrix.

[BibT_eX]

[DOI]

Pattern Recognit., 2019

An Internal Validity Index Based on Density-Involved Distance.

[BibT_eX]

[DOI]

Lianyu Hu

Caiming Zhong

IEEE Access, 2019

Lianyu Hu

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