William M. Pottenger

55 papers receiving 636 citations

Peers

William M. Pottenger
Comparison fields: 5 of 80
  • Artificial Intelligence 456
  • Information Systems 200
  • Computer Vision and Pattern Recognition 144
  • Computer Networks and Communications 91
  • Signal Processing 90
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Countries citing papers authored by William M. Pottenger

Since Specialization
Citations

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

Fields of papers citing papers by William M. Pottenger

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by William M. Pottenger. 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 William M. Pottenger. The network helps show where William M. Pottenger may publish in the future.

Co-authorship network of co-authors of William M. Pottenger

This figure shows the co-authorship network connecting the top 25 collaborators of William M. Pottenger. A scholar is included among the top collaborators of William M. Pottenger 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 William M. Pottenger. William M. Pottenger 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
#WorkIndexed citations
1 2
2
Modeling Microtext with Higher Order Learning
3
3 1
4 2
5
A semi-supervised active learning algorithm for information extraction from textual data: Research Articles
11
6
Methodologies for Trend Detection in Textual Data Mining
24
7 17
8 11
9 13
10 3
11 24
12 5
13
Multimedia for Computer Science: from CS0/CS1 to Grades 7-12
9
14 1
15 2
16
A Mathematical View of Latent Semantic Indexing: Tracing Term Co-Occurrences
10
17
CIMEL: constructive, collaborative inquiry-based multimedia E-learning.
10
18
Detecting emerging concepts in textual data mining
26
19 16
20
Theory, techniques, and experiments in solving recurrences in computer programs
8

About William M. Pottenger

William M. Pottenger is a scholar working on Hardware and Architecture, Artificial Intelligence and Information Systems, having authored 57 papers that have together received 717 indexed citations. Recurring topics across this work include Data Mining Algorithms and Applications (11 papers), Natural Language Processing Techniques (8 papers) and Rough Sets and Fuzzy Logic (7 papers). The work is most often cited by research in Artificial Intelligence (456 citations), Information Systems (200 citations) and Signal Processing (90 citations). William M. Pottenger has collaborated with scholars based in United States, Bulgaria and Netherlands. Frequent co-authors include April Kontostathis, Mark G. Arnold, Faisal M. Khan, Murat Can Ganiz, Glenn D. Blank, Brian Fisher, Bruce R. Schatz, William Ribarsky, G. Drew Kessler and Mooi Choo Chuah. Their work appears in journals such as IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on Parallel and Distributed Systems and Information Processing & Management.

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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