Bayesian classification (AutoClass): theory and results
- Authors
- Peter CheesemanJohn Stutz
- Journal
- Knowledge Discovery and Data Mining
In The Last Decade
doi.org/w6850779 →Countries where authors are citing Bayesian classification (AutoClass): theory and results
This map shows the geographic impact of Bayesian classification (AutoClass): theory and results. 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 Bayesian classification (AutoClass): theory and results with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Bayesian classification (AutoClass): theory and results more than expected).
Fields of papers citing Bayesian classification (AutoClass): theory and results
This network shows the impact of Bayesian classification (AutoClass): theory and results. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Bayesian classification (AutoClass): theory and results.
About Bayesian classification (AutoClass): theory and results
This paper, published in 1996, received 666 indexed citations . Written by Peter Cheeseman and John Stutz. It is primarily cited by scholars working on Artificial Intelligence (413 citations), Information Systems (203 citations) and Signal Processing (176 citations). Published in Knowledge Discovery and Data Mining.
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.
This paper is also available at doi.org/w6850779.