Kenneth A. Kaufman

1.3k total citations
45 papers, 446 citations indexed

About

Kenneth A. Kaufman is a scholar working on Artificial Intelligence, Information Systems and Computer Networks and Communications. According to data from OpenAlex, Kenneth A. Kaufman has authored 45 papers receiving a total of 446 indexed citations (citations by other indexed papers that have themselves been cited), including 24 papers in Artificial Intelligence, 12 papers in Information Systems and 8 papers in Computer Networks and Communications. Recurrent topics in Kenneth A. Kaufman's work include Data Mining Algorithms and Applications (10 papers), Evolutionary Algorithms and Applications (8 papers) and Neural Networks and Applications (6 papers). Kenneth A. Kaufman is often cited by papers focused on Data Mining Algorithms and Applications (10 papers), Evolutionary Algorithms and Applications (8 papers) and Neural Networks and Applications (6 papers). Kenneth A. Kaufman collaborates with scholars based in United States, Poland and United Kingdom. Kenneth A. Kaufman's co-authors include Ryszard S. Michalski, Larry Kerschberg, Janusz Wojtusiak, Piotr A. Domanski, David A. Yashar, Janusz Wnek, Eric Bloedorn, Guido Cervone, Liviu Panait and Alan C. Schultz and has published in prestigious journals such as International Journal of Intelligent Systems, Journal of Intelligent Information Systems and Fundamenta Informaticae.

In The Last Decade

Kenneth A. Kaufman

43 papers receiving 345 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Kenneth A. Kaufman United States 10 248 180 123 87 67 45 446
Ignacio J. Blanco Spain 9 209 0.8× 202 1.1× 104 0.8× 70 0.8× 68 1.0× 24 349
Eric Bloedorn United States 12 318 1.3× 108 0.6× 44 0.4× 58 0.7× 91 1.4× 24 410
Jianchao Han United States 8 98 0.4× 123 0.7× 41 0.3× 35 0.4× 37 0.6× 47 281
Enn Tyugu Estonia 10 146 0.6× 114 0.6× 44 0.4× 23 0.3× 66 1.0× 54 272
Zongtian Liu China 11 195 0.8× 112 0.6× 114 0.9× 43 0.5× 37 0.6× 77 325
Rong Peng China 13 259 1.0× 180 1.0× 19 0.2× 99 1.1× 53 0.8× 42 438
Detlef Plump United Kingdom 11 321 1.3× 120 0.7× 155 1.3× 8 0.1× 55 0.8× 40 463
Ian Bayley United Kingdom 11 254 1.0× 232 1.3× 24 0.2× 30 0.3× 101 1.5× 44 396
Bhabesh Nath India 8 225 0.9× 244 1.4× 154 1.3× 37 0.4× 18 0.3× 22 358
Kawuu W. Lin Taiwan 14 112 0.5× 194 1.1× 75 0.6× 124 1.4× 183 2.7× 49 482

Countries citing papers authored by Kenneth A. Kaufman

Since Specialization
Citations

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

Fields of papers citing papers by Kenneth A. Kaufman

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Kenneth A. Kaufman. 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 Kenneth A. Kaufman. The network helps show where Kenneth A. Kaufman may publish in the future.

Co-authorship network of co-authors of Kenneth A. Kaufman

This figure shows the co-authorship network connecting the top 25 collaborators of Kenneth A. Kaufman. A scholar is included among the top collaborators of Kenneth A. Kaufman 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 Kenneth A. Kaufman. Kenneth A. Kaufman 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.
Michalski, Ryszard S., Janusz Wojtusiak, & Kenneth A. Kaufman. (2006). Intelligent Optimization via Learnable Evolution Model. 38. 332–335. 1 indexed citations
2.
Michalski, Ryszard S., et al.. (2005). Learning User Models for Computer Intrusion Detection: Preliminary Results from Natural Induction Approach. George Mason University. 2 indexed citations
3.
Domanski, Piotr A., David A. Yashar, Kenneth A. Kaufman, & Ryszard S. Michalski. (2004). An Optimized Design of Finned-Tube Evaporators Using the Learnable Evolution Model. HVAC&R Research. 10(2). 201–211. 43 indexed citations
4.
Cervone, Guido, Kenneth A. Kaufman, & Ryszard S. Michalski. (2002). Recent Results from the Experimental Evaluation of the Learnable Evolution Model. George Mason University. 47–62. 2 indexed citations
5.
Michalski, Ryszard S. & Kenneth A. Kaufman. (2001). The AQ19 System for Machine Learning and Pattern Discovery: A General Description and User's Guide. George Mason University. 17 indexed citations
6.
Michalski, Ryszard S. & Kenneth A. Kaufman. (2000). The AQ18 System for Machine Learning and Data Mining System: An Implementation and User's Guide. George Mason University. 7 indexed citations
7.
Cervone, Guido, Ryszard S. Michalski, Kenneth A. Kaufman, & Liviu Panait. (2000). Combining Machine Learning with Evolutionary Computation: Recent Results on LEM. George Mason University. 6 indexed citations
8.
Kaufman, Kenneth A. & Ryszard S. Michalski. (2000). Applying Learnable Evolution Model to Heat Exchanger Design. George Mason University. 1014–1019. 9 indexed citations
9.
Michalski, Ryszard S. & Kenneth A. Kaufman. (2000). ISHED1: Applying the LEM Methodology to Heat Exchanger Design. George Mason University. 3 indexed citations
10.
Michalski, Ryszard S. & Kenneth A. Kaufman. (1999). A Measure of Description Quality for Data Mining and its Implementation in the AQ18 Learning System. George Mason University. 3 indexed citations
11.
Kaufman, Kenneth A. & Ryszard S. Michalski. (1999). Learning in an Inconsistent World: Rule Selection in AQ18. George Mason University. 5 indexed citations
12.
Kaufman, Kenneth A.. (1998). INLEN: a methodology and integrated system for knowledge discovery in databases. 7 indexed citations
13.
Michalski, Ryszard S. & Kenneth A. Kaufman. (1997). Data Mining and Knowledge Discovery: A Review of Issues and a Multistrategy Approach. George Mason University. 40 indexed citations
14.
Kaufman, Kenneth A. & Ryszard S. Michalski. (1996). A method for reasoning with structured and continuous attributes in the INLEN-2 multistrategy knowledge discovery system. Knowledge Discovery and Data Mining. 232–237. 12 indexed citations
15.
Kaufman, Kenneth A., et al.. (1995). Knowledge discovery from multiple databases. Knowledge Discovery and Data Mining. 240–245. 31 indexed citations
16.
Kaufman, Kenneth A.. (1994). Comparing international development patterns using multi-operator learning and discovery tools. Knowledge Discovery and Data Mining. 431–440. 6 indexed citations
17.
Michalski, Ryszard S., et al.. (1992). Mining for knowledge in databases: The INLEN architecture, initial implementation and first results. Journal of Intelligent Information Systems. 1(1). 85–113. 52 indexed citations
18.
Kaufman, Kenneth A., Ryszard S. Michalski, & Larry Kerschberg. (1991). An Architecture for Integrating Machine Learning and Discovery Programs into a Data Analysis System. George Mason University. 5 indexed citations
19.
Kaufman, Kenneth A., Ryszard S. Michalski, & Alan C. Schultz. (1989). EMERALD 1: An Integrated System of Machine Learning and Discovery Programs for Education and Research, User's Guide. George Mason University. 4 indexed citations
20.
Belford, Geneva G., et al.. (1984). Mutual consistency maintenance in a prototype Data Traffic Management System. 596–602. 1 indexed citations

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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