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The WEKA data mining software: an update
Open accessAbstract
More than twelve years have elapsed since the first public release of WEKA. In that time, the software has been rewritten entirely from scratch, evolved substantially and now accompanies a text on data mining [35]. These days, WEKA enjoys widespread acceptance in both academia and business, has an active community, and has been downloaded more than 1.4 million times since being placed on Source-Forge in April 2000. This paper provides an introduction to the WEKA workbench, reviews the history of the project, and, in light of the recent 3.6 stable release, briefly discusses what has been added since the last stable version (Weka 3.4) released in 2003.
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“The authors exploit several characteristics that can be gathered crawling an account's details, both from its profile and timeline. For each investigated account, such characteristics are exploited in a Random Forest algorithm [23, 43], that outputs if the account is a spambot or not.”
From Fame for sale: Efficient detection of fake Twitter followers (2015) · cites this paper as [43]
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“the Waikato Environment for Knowledge Analysis (Weka) tool is a relatively accessible and easy to use software package for applying machine learning algorithms for data mining tasks [32].”
From Opening the Black Box of Electronic Health: Collecting, Analyzing, and Interpreting Log Data (2017) · cites this paper as [32]
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Metadata
- Authors
- Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H.
- Affiliations
- Pentaho Corporation, Orlando, FL
- University of Waikato (NZ)
- Venue
- ACM SIGKDD Explorations Newsletter
- Published
- 2009-11-16
- Volume
- 11
- Issue
- 1
- Pages
- 10-18
- ISSN
- 1931-0145 (ISSN-L)
- 1931-0153
- OpenAlex
- W2133990480
- Cited by
- 17,750
- References
- 37
- Citations by year
- 2026: 93 · 2025: 198 · 2024: 252 · 2023: 342 · 2022: 473 · 2021: 742 · 2020: 949 · 2019: 1162 · 2018: 1382 · 2017: 1550 · 2016: 2009 · 2015: 2093 · 2014: 2031 · 2013: 1714 · 2012: 1353
- FWCI
- 2427.11
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Locations
- ACM SIGKDD Explorations Newsletter (publishedVersion)
- Research Commons (University of Waikato) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Data Mining and Machine Learning Applications
- Physical Sciences › Computer Science › Information Systems › Data Mining Algorithms and Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Keywords
- WEKA, data mining tools, software release history, open source software
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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