James Bailey
- Computational Mathematics top 1%
- Artificial Intelligence top 0.2%
- Advanced Clustering Algorithms Research 20
- Anomaly Detection Techniques and Applications 19
- Adversarial Robustness in Machine Learning 13
- Signal Processing top 0.5%
- Data Management and Algorithms 30
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- Data Mining Algorithms and Applications 25
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- Complex Network Analysis Techniques 23
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- Advanced Database Systems and Queries 17
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- Surgical Simulation and Training 14
- Journals
- Knowledge and Information Systems (6 papers)Data Mining and Knowledge Discovery (5 papers)Pattern Recognition (5 papers)
- Partner nations
- AustraliaUnited StatesChina
In The Last Decade
James Bailey
206 papers receiving 6.6k citations
Hit Papers
Peers
Comparison fields: 5 of 203
- Computational Mathematics 115
- Artificial Intelligence 3.9k
- Signal Processing 963
- Computer Vision and Pattern Recognition 1.6k
- Computer Science Applications 276
Countries citing papers authored by James Bailey
This map shows the geographic impact of James Bailey's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by James Bailey with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites James Bailey more than expected).
Fields of papers citing papers by James Bailey
This network shows the impact of papers produced by James Bailey. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by James Bailey. The network helps show where James Bailey may publish in the future.
Co-authorship network
The 25 scholars most cited alongside James Bailey, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 1 | |
| 2 | 2024 | 0 | |
| 3 | 2024 | 0 | |
| 4 | 2023 | 4 | |
| 5 | 2023 | 1 | |
| 6 | 2023 | 2 | |
| 7 | 2022 | 7 | |
| 8 | 2020 | 134 | |
| 9 | 2020 | 14 | |
| 10 | Improving Adversarial Robustness Requires Revisiting Misclassified Examples | 2020 | 153 |
| 11 | 2018 | 24 | |
| 12 | Dimensionality-Driven Learning with Noisy Labels | 2018 | 49 |
| 13 | Generalized Modularity for Community Detection | 2015 | 1 |
| 14 | FILTA: Better View Discovery from Collections of Clusterings via Filtering | 2014 | 1 |
| 15 | Standardized Mutual Information for Clustering Comparisons: One Step Further in Adjustment for Chance | 2014 | 42 |
| 16 | 2012 | 2 | |
| 17 | Information Theoretic Measures for Clusterings Comparison: Variants, Properties, Normalization and Correction for Chancebreakdown → | 2010 | 1200 |
| 18 | A query based approach for mining evolving graphs | 2009 | 7 |
| 19 | Are zero-suppressed binary decision diagrams good for mining frequent patterns in high dimensional datasets? | 2007 | 5 |
| 20 | Static analysis of XSLT programs | 2004 | 11 |
About James Bailey
James Bailey is a scholar working on Computational Mathematics, Signal Processing and Artificial Intelligence, having authored 215 papers that have together received 6.8k indexed citations. Recurring topics across this work include Data Management and Algorithms (30 papers), Data Mining Algorithms and Applications (25 papers), Complex Network Analysis Techniques (23 papers), Advanced Clustering Algorithms Research (20 papers), Anomaly Detection Techniques and Applications (19 papers), Advanced Database Systems and Queries (17 papers), Surgical Simulation and Training (14 papers) and Adversarial Robustness in Machine Learning (13 papers). The work is most often cited by research in Computational Mathematics (115 citations), Artificial Intelligence (3.9k citations) and Signal Processing (963 citations). James Bailey has collaborated with scholars based in Australia, United States and China. Frequent co-authors include Nguyễn Xuân Vinh, Julien Epps, Xingjun Ma, Yisen Wang, Jinfeng Yi, Yuan Luo, Jeffrey Chan, Guozhu Dong, Kotagiri Ramamohanarao and Simone Romano. Their work appears in journals such as Knowledge and Information Systems, Data Mining and Knowledge Discovery, Pattern Recognition, Machine Learning and BMC Bioinformatics.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.