Cluster Summarization is a computational method that generates concise, interpretable representations of clusters by identifying and synthesizing the most representative features or exemplars within each cluster to facilitate understanding and analysis in data visualization.
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2017–2026
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38
CACTUS—clustering categorical data using summaries
Johannes Gehrke, Raghu Ramakrishnan · 1999 · 470 citations
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115
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1–5 of 115
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67
University of Missouri
Columbia, United States
3 h-index
13 publications
312 citations
University of Waterloo
Waterloo, Canada
2 h-index
3 publications
110 citations
Drexel University
Philadelphia, United States
2 h-index
2 publications
39 citations
University of Piraeus
Piraeus, Greece
1 h-index
1 publications
11 citations
Georgia Institute of Technology
Atlanta, United States
1 h-index
2 publications
116 citations
University of West Florida
Pensacola, United States
1 h-index
1 publications
20 citations
University of Missouri Health System
Columbia, United States
1 h-index
2 publications
40 citations
Carnegie Mellon University
Pittsburgh, United States
1 h-index
1 publications
15 citations
1–8 of 67
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- ACM Transactions on Information Systems
1 publications · 58 citations
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- Machine Learning
1 publications · 15 citations
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- Information Sciences
1 publications · 36 citations
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- Information Fusion
1 publications · 25 citations
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- Tsinghua Science & Technology
1 publications · 23 citations
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