Kris E. Anderson
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Researcher
Unknown Researcher
Research Areas
Biography and Research Information
OverviewAI-generated summary
Kris E. Anderson's research focuses on unsupervised methods for data management, specifically in areas of data clustering, cleaning, and entity resolution. Anderson has published work on optimal starting parameters for unsupervised data clustering and cleaning within a "Data Washing Machine" framework. Additionally, Anderson has contributed to the development of scalable, MapReduce-based designs for unsupervised entity resolution systems. Collaborations include work with Nicholas Kofi Akortia Hagan, John R. Talburt, and Deasia Hagan, all from the University of Arkansas at Little Rock, with whom Anderson shares multiple publications.
Metrics
- h-index: 2
- Publications: 2
- Citations: 7
Selected Publications
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A scalable MapReduce-based design of an unsupervised entity resolution system (2024)
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Optimal Starting Parameters for Unsupervised Data Clustering and Cleaning in the Data Washing Machine (2023)
Collaboration Network
Top Collaborators
- Optimal Starting Parameters for Unsupervised Data Clustering and Cleaning in the Data Washing Machine
- A scalable MapReduce-based design of an unsupervised entity resolution system
- Optimal Starting Parameters for Unsupervised Data Clustering and Cleaning in the Data Washing Machine
- A scalable MapReduce-based design of an unsupervised entity resolution system
- Optimal Starting Parameters for Unsupervised Data Clustering and Cleaning in the Data Washing Machine
- A scalable MapReduce-based design of an unsupervised entity resolution system
- Optimal Starting Parameters for Unsupervised Data Clustering and Cleaning in the Data Washing Machine
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