Richard C. Dubes

11.3k total citations · 1 hit paper
42 papers, 7.6k citations indexed

About

Richard C. Dubes is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Signal Processing. According to data from OpenAlex, Richard C. Dubes has authored 42 papers receiving a total of 7.6k indexed citations (citations by other indexed papers that have themselves been cited), including 21 papers in Artificial Intelligence, 18 papers in Computer Vision and Pattern Recognition and 9 papers in Signal Processing. Recurrent topics in Richard C. Dubes's work include Image Retrieval and Classification Techniques (10 papers), Advanced Clustering Algorithms Research (9 papers) and Bayesian Methods and Mixture Models (7 papers). Richard C. Dubes is often cited by papers focused on Image Retrieval and Classification Techniques (10 papers), Advanced Clustering Algorithms Research (9 papers) and Bayesian Methods and Mixture Models (7 papers). Richard C. Dubes collaborates with scholars based in United States, United Kingdom and Germany. Richard C. Dubes's co-authors include Warren S. Sarle, Anil K. Jain, Thomas A. Bailey, Chien-Chang Chen, Karl Pettis, Jacob R. Lorch, Qian Huang, Gautam Biswas, Guangzhou Zeng and Xiaobo Li and has published in prestigious journals such as Journal of the American Statistical Association, IEEE Transactions on Pattern Analysis and Machine Intelligence and Technometrics.

In The Last Decade

Richard C. Dubes

38 papers receiving 6.8k citations

Hit Papers

Algorithms for Clustering Data 1990 2026 2002 2014 1990 1000 2.0k 3.0k 4.0k 5.0k
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Algorithms for Clustering Data
    1990 5.5k citations Richard C. Dubes et al. profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Richard C. Dubes United States 19 3.8k 2.6k 1.5k 1.1k 648 42 7.6k
Warren S. Sarle United States 9 3.7k 1.0× 2.0k 0.8× 1.5k 1.0× 1.3k 1.2× 806 1.2× 11 8.5k
Sheelagh Lloyd United States 4 2.9k 0.8× 2.8k 1.1× 1.2k 0.8× 544 0.5× 588 0.9× 6 9.6k
Bernhard Flury Switzerland 15 2.7k 0.7× 1.5k 0.6× 1.0k 0.7× 765 0.7× 544 0.8× 24 6.8k
J. C. Dunn United States 17 3.1k 0.8× 1.8k 0.7× 876 0.6× 596 0.6× 444 0.7× 48 7.2k
Mark E. Shields United States 3 4.3k 1.1× 3.1k 1.2× 814 0.6× 494 0.5× 981 1.5× 6 10.7k
Jianchang Mao United States 19 3.7k 1.0× 3.2k 1.2× 1.1k 0.8× 779 0.7× 530 0.8× 43 9.9k
Ah Chung Tsoi Australia 31 5.1k 1.4× 2.8k 1.1× 1.2k 0.8× 913 0.8× 705 1.1× 184 11.0k
Carla E. Brodley United States 44 5.0k 1.3× 2.4k 0.9× 1.5k 1.0× 1.4k 1.3× 828 1.3× 132 9.8k
Mohamed S. Kamel Canada 44 5.0k 1.3× 3.6k 1.4× 2.6k 1.7× 1.4k 1.3× 371 0.6× 404 11.2k
Krzysztof J. Cios United States 29 6.3k 1.7× 3.7k 1.4× 1.2k 0.8× 1.3k 1.2× 1.6k 2.5× 128 14.8k

Countries citing papers authored by Richard C. Dubes

Since Specialization
Citations

This map shows the geographic impact of Richard C. Dubes'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 Richard C. Dubes with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Richard C. Dubes more than expected).

Fields of papers citing papers by Richard C. Dubes

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Richard C. Dubes. 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 Richard C. Dubes. The network helps show where Richard C. Dubes may publish in the future.

Co-authorship network of co-authors of Richard C. Dubes

This figure shows the co-authorship network connecting the top 25 collaborators of Richard C. Dubes. A scholar is included among the top collaborators of Richard C. Dubes based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Richard C. Dubes. Richard C. Dubes is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Dubes, Richard C., et al.. (2016). Unethical Fitting and Showing Practices in Junior Livestock Shows. 5. 99–106.
2.
Chen, Chien-Chang & Richard C. Dubes. (2002). Discrete MRF model parameters as features for texture classification. pami 9. 1–6. 1 indexed citations
3.
Huang, Qian, Jacob R. Lorch, & Richard C. Dubes. (1994). Can the fractal dimension of images be measured?. Pattern Recognition. 27(3). 339–349. 87 indexed citations
4.
Dubes, Richard C. & Anil K. Jain. (1993). Random field models in image analysis*. Journal of Applied Statistics. 20(5-6). 121–154. 8 indexed citations
5.
Dubes, Richard C., et al.. (1992). Book reviews. Journal of Classification. 9(2). 291–306.
6.
Dubes, Richard C. & Michael P. Windham. (1990). CSNA-90 Program Classification and Clustering: Perspectives and Prospects June 21 – 23 Utah State University Logan, Utah, USA. Journal of Classification. 7(2). 317–319.
7.
Dubes, Richard C., et al.. (1989). Experiments in projection and clustering by simulated annealing. Pattern Recognition. 22(2). 213–220. 123 indexed citations
8.
Dubes, Richard C. & Anil K. Jain. (1989). Random field models in image analysis. Journal of Applied Statistics. 16(2). 131–164. 300 indexed citations
9.
Jain, Anil K. & Richard C. Dubes. (1988). Algorithms for clustering data. 2 indexed citations
10.
Jain, Anil K., Richard C. Dubes, & Chien-Chang Chen. (1987). Bootstrap Techniques for Error Estimation. IEEE Transactions on Pattern Analysis and Machine Intelligence. PAMI-9(5). 628–633. 143 indexed citations
11.
Dubes, Richard C. & Guangzhou Zeng. (1987). A test for spatial homogeneity in cluster analysis. Journal of Classification. 4(1). 33–56. 16 indexed citations
12.
Dubes, Richard C.. (1987). How many clusters are best? - An experiment. Pattern Recognition. 20(6). 645–663. 157 indexed citations
13.
Li, Xiaobo & Richard C. Dubes. (1985). The First Stage in Two-Stage Template Matching. IEEE Transactions on Pattern Analysis and Machine Intelligence. PAMI-7(6). 700–707. 11 indexed citations
14.
Zeng, Guangzhou & Richard C. Dubes. (1985). A test for spatial randomness based on k-NN distances. Pattern Recognition Letters. 3(2). 85–91. 8 indexed citations
15.
Dubes, Richard C., et al.. (1983). A review of ridge counting in dermatoglyphics. Pattern Recognition. 16(1). 1–8. 16 indexed citations
16.
Biswas, Gautam, et al.. (1981). Evaluation of Projection Algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence. PAMI-3(6). 701–708. 66 indexed citations
17.
Dubes, Richard C., et al.. (1980). Stability of a hierarchical clustering. Pattern Recognition. 12(3). 177–187. 28 indexed citations
18.
Pettis, Karl, Thomas A. Bailey, Anil K. Jain, & Richard C. Dubes. (1979). An Intrinsic Dimensionality Estimator from Near-Neighbor Information. IEEE Transactions on Pattern Analysis and Machine Intelligence. PAMI-1(1). 25–37. 151 indexed citations
19.
Dubes, Richard C., et al.. (1979). Single-link characteristics of a mode-seeking clustering algorithm. Pattern Recognition. 11(1). 65–70. 9 indexed citations
20.
Dubes, Richard C., et al.. (1969). CLASSIFICATION OF ELECTRO-ENCEPHALOGRAMS WITH PATTERN RECOGNITION ALGORITHMS.. Defense Technical Information Center (DTIC).

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.

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