Daniel T. Larose
Impact in
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- Artificial Intelligence in Healthcare
- Information Systems top 2%
- Data Mining and Machine Learning Applications
- Data Mining Algorithms and Applications
- Multimedia Learning Systems
Papers in
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- Data Mining Algorithms and Applications 4
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- Meta-analysis and systematic reviews 2
- Co-authors
- Chantal D. Larose (5 shared papers)Dipak K. Dey (3 shared papers)Alan M. Jette (1 shared paper)Mary D. Slavin (1 shared paper)David Rosenblum (1 shared paper)S. Seetharama (1 shared paper)Bethlyn Houlihan (1 shared paper)Randall W. Barton (2 shared papers)
- Journals
- Neuromuscular Disorders (1 paper)Statistics in Medicine (1 paper)Archives of Physical Medicine and Rehabilitation (1 paper)Computational Statistics & Data Analysis (1 paper)Test (1 paper)
- Partner nations
- United StatesNorwayFrance
In The Last Decade
Daniel T. Larose
13 papers receiving 1.1k citations
Daniel T. Larose's Hit Papers
Peers
Comparison fields: 5 of 172
- Health Information Management 61
- Information Systems 277
- Artificial Intelligence 352
- Statistics, Probability and Uncertainty 51
- Signal Processing 76
Countries citing papers authored by Daniel T. Larose
This map shows the geographic impact of Daniel T. Larose'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 Daniel T. Larose with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel T. Larose more than expected).
Fields of papers citing papers by Daniel T. Larose
This network shows the impact of papers produced by Daniel T. Larose. 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 Daniel T. Larose. The network helps show where Daniel T. Larose may publish in the future.
Co-authors
The 14 scholars most cited alongside Daniel T. Larose, 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 | Discovering Knowledge in Data Hit paper breakdown → | 2014 | 359 |
| 2 | 2004 | 338 | |
| 3 | 2005 | 258 | |
| 4 | Data Mining and Predictive Analytics | 2015 | 61 |
| 5 | 2016 | 45 | |
| 6 | 1997 | 37 | |
| 7 | 2018 | 24 | |
| 8 | 1998 | 21 | |
| 9 | 2019 | 12 | |
| 10 | 1996 | 10 | |
| 11 | 2006 | 8 | |
| 12 | 2013 | 2 | |
| 13 | Discovering knowledge in Data: an Introduction Data Mining. Ed. 2 | 2019 | 1 |
| 14 | 2015 | 0 |
About Daniel T. Larose
Daniel T. Larose is a scholar working on Information Systems, Statistics, Probability and Uncertainty, Statistics and Probability, Artificial Intelligence and Molecular Biology, having authored 14 papers that have together received 1.2k indexed citations. Recurring topics across this work include Data Mining Algorithms and Applications (4 papers), Statistical Methods and Bayesian Inference (3 papers), Meta-analysis and systematic reviews (2 papers), Semantic Web and Ontologies (2 papers), Statistical Distribution Estimation and Applications (1 paper), Muscle Physiology and Disorders (1 paper), Spinal Cord Injury Research (1 paper) and Medieval and Classical Philosophy (1 paper). The work is most often cited by research in Health Information Management (61 citations), Information Systems (277 citations), Artificial Intelligence (352 citations), Statistics, Probability and Uncertainty (51 citations) and Signal Processing (76 citations). Daniel T. Larose has collaborated with scholars based in United States, Norway and France. Frequent co-authors include Chantal D. Larose, Dipak K. Dey, Alan M. Jette, Mary D. Slavin, David Rosenblum, S. Seetharama, Bethlyn Houlihan, Randall W. Barton, Zdravko Markov and Qian Wu. Their work appears in journals such as Neuromuscular Disorders, Statistics in Medicine, Archives of Physical Medicine and Rehabilitation, Computational Statistics & Data Analysis and Test.
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.