Dan Steinberg
- Artificial Intelligence top 0.5%
- Neural Networks and Applications 2
- Information Systems top 0.5%
- Data Mining Algorithms and Applications 2
- Health Information Management top 0.5%
- Signal Processing top 2%
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- Economic and Environmental Valuation 5
- Economics of Agriculture and Food Markets 2
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- Decision-Making and Behavioral Economics 2
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- Consumer Market Behavior and Pricing 2
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- Social Media and Politics 1
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- Water Quality and Resources Studies 1
Dan Steinberg
15 papers receiving 4.1k citations
Hit Papers
Peers
Comparison fields: 5 of 201
- Artificial Intelligence 1.8k
- Information Systems 886
- Health Information Management 182
- Signal Processing 349
- Computer Vision and Pattern Recognition 555
Countries citing papers authored by Dan Steinberg
This map shows the geographic impact of Dan Steinberg'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 Dan Steinberg with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dan Steinberg more than expected).
Fields of papers citing papers by Dan Steinberg
This network shows the impact of papers produced by Dan Steinberg. 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 Dan Steinberg. The network helps show where Dan Steinberg may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Dan Steinberg, 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 | 2021 | 2 | |
| 2 | Top 10 algorithms in data miningbreakdown → | 2007 | 3897 |
| 3 | 2007 | 1 | |
| 4 | Identifying SPAM with Predictive Models | 2006 | 1 |
| 5 | Stochastic Gradient Boosting: An Introduction to TreeNet™. | 2002 | 1 |
| 6 | An alternative to neural nets: multivariate adaptive regression splines (MARS) | 2001 | 6 |
| 7 | The hybrid CART-Logit model in classification and data mining | 1998 | 18 |
| 8 | 1997 | 70 | |
| 9 | 1994 | 218 | |
| 10 | 1992 | 19 | |
| 11 | 1990 | 30 | |
| 12 | 1989 | 4 | |
| 13 | Experimental Design for Discrete Choice Voter Preference Surveys | 1989 | 11 |
| 14 | 1989 | 71 | |
| 15 | 1988 | 1 |
About Dan Steinberg
Dan Steinberg is a scholar working on General Decision Sciences, Business and International Management, Marketing, Economics and Econometrics and Communication, having authored 15 papers that have together received 4.3k indexed citations. Recurring topics across this work include Economic and Environmental Valuation (5 papers), Neural Networks and Applications (2 papers), Economics of Agriculture and Food Markets (2 papers), Decision-Making and Behavioral Economics (2 papers), Consumer Market Behavior and Pricing (2 papers), Data Mining Algorithms and Applications (2 papers), Social Media and Politics (1 paper) and Water Quality and Resources Studies (1 paper). The work is most often cited by research in Artificial Intelligence (1.8k citations), Information Systems (886 citations), Health Information Management (182 citations), Signal Processing (349 citations) and Computer Vision and Pattern Recognition (555 citations). Dan Steinberg has collaborated with scholars based in United States, Australia and Japan. Frequent co-authors include Shu‐Kay Ng, Michael Steinbach, Xindong Wu, Philip S. Yu, Geoffrey J. McLachlan, David J. Hand, Bing Liu, J. R. Quinlan, Joydeep Ghosh and Hiroshi Motoda. Their work appears in journals such as The American Statistician, Marketing Letters, Kennedy Institute of Ethics journal, International Journal of Data Warehousing and Mining and Journal of Econometrics.
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.