Richard Kirkby
- Artificial Intelligence top 1%
- Signal Processing top 2%
- Computer Networks and Communications top 5%
- Information Systems top 5%
- Management Science and Operations Research top 5%
- Co-authors
- Albert BifetGeoffrey HolmesBernhard PfahringerRicard GavaldàEibe FrankPeter ReutemannRemco BouckaertDavid Bainbridge
- Topics
- Data Stream Mining Techniques (7 papers)Data Mining Algorithms and Applications (6 papers)Machine Learning and Data Classification (4 papers)
- Journals
- Journal of Machine Learning ResearchActa HorticulturaeResearch Commons (University of Waikato)
- Partner nations
- New ZealandSpain
In The Last Decade
Richard Kirkby
9 papers receiving 1.3k citations
Hit Papers
Peers
Comparison fields: 5 of 87
- Artificial Intelligence 1.2k
- Signal Processing 358
- Computer Networks and Communications 310
- Information Systems 174
- Management Science and Operations Research 118
Countries citing papers authored by Richard Kirkby
This map shows the geographic impact of Richard Kirkby'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 Kirkby with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Richard Kirkby more than expected).
Fields of papers citing papers by Richard Kirkby
This network shows the impact of papers produced by Richard Kirkby. 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 Kirkby. The network helps show where Richard Kirkby may publish in the future.
Co-authorship network of co-authors of Richard Kirkby
This figure shows the co-authorship network connecting the top 25 collaborators of Richard Kirkby. A scholar is included among the top collaborators of Richard Kirkby 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 Kirkby. Richard Kirkby is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | MOA: Massive Online Analysisbreakdown → | 703 |
| 2 | Massive Online Analysis | 46 |
| 3 | New ensemble methods for evolving data streamsbreakdown → | 371 |
| 4 | DATA STREAM MINING A Practical Approach | 142 |
| 5 | WEKA Manual for Version 3-6-10 | 73 |
| 6 | 12 | |
| 7 | 2 | |
| 8 | Batch-Incremental Learning for Mining Data Streams | 7 |
| 9 | Mining data streams using option trees (revised edition, 2004) | 0 |
| 10 | Mining data streams using option trees | 3 |
About Richard Kirkby
Richard Kirkby is a scholar working on Signal Processing, Artificial Intelligence and Information Systems, having authored 10 papers that have together received 1.4k indexed citations. Recurring topics across this work include Data Stream Mining Techniques (7 papers), Data Mining Algorithms and Applications (6 papers) and Machine Learning and Data Classification (4 papers). The work is most often cited by research in Artificial Intelligence (1.2k citations), Signal Processing (358 citations) and Computer Networks and Communications (310 citations). Richard Kirkby has collaborated with scholars based in New Zealand and Spain. Frequent co-authors include Albert Bifet, Geoffrey Holmes, Bernhard Pfahringer, Ricard Gavaldà, Eibe Frank, Peter Reutemann, Remco Bouckaert, David Bainbridge and A.D. Mowat. Their work appears in journals such as Journal of Machine Learning Research, Acta Horticulturae and Research Commons (University of Waikato).
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.